The proposed JMIM method outperforms five competing methods in terms of accuracy . Feature selection is used in many application areas relevant to expert and intelligent systems , such as data mining and machine learning , image processing , anomaly detection , bioinformatics and natural language processing . Feature selection based on information theory is a popular approach due its computational efficiency , scalability in terms of the dataset dimensionality , and independence from the classifier . First , spectral , extend morphological profiles ( EMPs ) , and Gabor texture features are captured from an input HSI . Then , we define the anomaly detection problem as an anomaly game model , in which image regions ( superpixels ) of different features are modeled as players who select to be “ anomaly ” or “ background ” as their strategies . Three initial detection results are produced based on each player ’s strategy in the Nash equilibrium of the anomaly game . Keywords : Intrusion Detection , Anomaly-based Detection , Signature-based detection Intrusion detection systems ( IDS ) raise alerts if any unusual network traffic is detected and thus remains critical for network safety . Utilization of machine learning led methodologies in anomaly-based detection has gained popularity in recent years . In this paper , we conduct a thorough survey of studies based on the intrusion detection systems by using machine learning algorithms and present comparison dependent on dataset used , data reduction approaches , type of classifiers used and the outcome achieved by such different algorithms . In recent years , medical wireless sensor networks suffer from several anomalies and defaults , which can cause the malfunctioning of the whole network , many anomaly detection mechanisms , have been proposed . Therefore , the anomaly detection process is a required function in the medical environment to help doctors in making appropriate and accurate decisions . We propose in this paper to enhance the anomaly detection algorithm by adding a correlation function using data from different body sensor types . Confident decisions upon the abnormality of a particular vessel behavior require a certain level of situation awareness that may be difficult to achieve when the operator is overloaded by the available information . Based on a visual analytics process model , we present a novel system that supports the acquisition of situation awareness and the involvement of the user in the anomaly detection process using two layers of interactive visualizations . The system uses an interactive data mining module that supports the insertion of the user 's knowledge and experience in the creation , validation and continuous update of the normal model of the environment . The efficient of detection is improved awfully . The model of anomaly detecting is used the technique of artificial immune . Our system worked well at detecting both known and new attacks . The K-th moment filter has the advantage of lower computational complexity and is more effective in on-line measurement system . On the other hand , the minimum entropy filter can remain more local tiny features in the processed signals by adjusting the parameters properly so that is better in anomaly detecting of the defects in different levels . Bayesian networks can be regarded as a powerful tool for macro programming a spatial computer , such as a dense sensor network , in a variety of data analysis applications . In this article we present our architecture to program a spatial computer by means of a distributed Bayesian network and present some applications we developed over a sensor network testing both inference and anomaly-detection analysis . Anomaly detection and distinction are in the thick of major challenges that network managers encounter . Complication of monitoring the broaden and complex WLANs , that often requires heavy instrumentation of the user devices , makes the anomaly detection analysis even harder . In this paper we exploit 802.11 access point usage data and propose an anomaly detection technique based on Hidden Markov Model ( HMM ) and Universal Background Model ( UBM ) on data that is inexpensive to obtain . It is critical for a reliable cloud to effectively find out root causes of the cloud service anomalies for efficacious treatment . System logs are widely used for anomaly detection and analysis . Many efforts have been made to handle massive cloud logs automatically . In order to deal with the unknown attack of the cloud platform and distinguish the behavior after the attack from the peak load under normal work , this paper studies a cloud data center anomaly detection method for Distributed Denial of Service(DDoS ) attacks . We built a virtual machine cluster on openstack , simulated DDoS attacks on virtual machines , collected CPU utilization and network traffic data before and after the attack , and performed anomaly detection and analysis through a single class classification algorithm and analysis of time window sequences . We evaluated the performance of this method through lab-based experiments and real-world cloud data center experiments . ( 4 ) The experiment result shows that MC-CNN has the highest accuracy compared with conventional machine learning methods . The result also proves that our approach has performed well not only in DDoS attack detection but also in other anomaly attack detection . There are described essential aspects of threats to safety of correct operation of a WSN network with regard to possible attack types . A two-stage method of anomaly/attack detection was proposed . Stage One consisted in finding and eliminating of any outlying observations in the analyzed parameters of the WSN network traffic by means of Cook ’s distance . For our study , we annotated and analyzed a new human pose dataset from a clinical setting to study the subject-wise sensitivity and accuracy of anomaly detection on our proposed variational autoencoder ( VAEs ) } based frameworks . For our study , we performed anomaly analysis and detection based on our frameworks with PatientPose , a 2D pose estimator designed for the clinic setting . We demonstrate a strategy to correct anomalous to improve pose estimation accuracy and quantify and consider design-tradeoffs for our anomalous pose detection method . It is an online learning algorithm which builds useful incremental results , and is trivially parallelizable . These qualities make it suitable for a broad class of real world applications such as network classification , and anomaly detection . section : INTRODUCTION Intrusion detection systems play an important role in numerous industrial applications , such as network security and abnormal event detection . They effectively protect our critical computer systems or networks against the network attackers . Automated visual surveillance systems are attracting extensive interest due to public security . In this paper , we attempt to mine semantic context information including object-specific context information and scene-specific context information ( learned from object-specific context information ) to build an intelligent system with robust object detection , tracking , and classification and abnormal event detection . By means of object-specific context information , a cotrained classifier , which takes advantage of the multiview information of objects and reduces the number of labeling training samples , is learned to classify objects into pedestrians or vehicles with high object classification performance . In the sparse online scheme , the model complexity is controlled by the coherence criterion . The online LS-OC-SVM is adopted to handle the abnormal event detection problem . Each frame of the video is characterized by the covariance matrix descriptor encoding the moving information , then is classified into a normal or an abnormal frame . This paper proposes a novel method to detect suspicious objects from videos for abnormal event analysis . When considering a robbery event happens , there should be some suspicious object transferring conditions following between the forager and the victim . After that , a spider-web structure is proposed for missing object detection even though there are large camera movements between any two adjacent frames . Experimental results prove that our proposed system is efficient , robust , and superior in missing object detection and abnormal event analysis . To detect abnormal events in crowded environment , low-level features of moving objects are extracted from video frames in optical-flow form . These features are used to get motion correlation model which is used in abnormal event detection phase . There are two steps in abnormal event detection phase . The first detection step is based on local model which is made with local information such as velocities , directions and positions and the second step is based on gobal model which is made with global information . We present a computational approach to abnormal visual event detection , which is based on exploring and modeling local motion patterns in a non-linear subspace . We use motion vectors extracted over a region of interest ( ROI ) as features and a non-linear , graph-based manifold learning algorithm coupled with a supervised novelty classifier to label segments of a video sequence . In addition , an extension of OPML ( namely COPML ) is further proposed to enhance the robustness when in real case the first several samples come from the same class ( i.e. , cold start problem ) . In the experiments , we have systematically evaluated our methods ( OPML and COPML ) on three typical tasks , including UCI data classification , face verification , and abnormal event detection in videos , which aims to fully evaluate the proposed methods on different sample number , different feature dimensionalities and different feature extraction ways ( i.e. , hand-crafted and deeply-learned ) . The results show that OPML and COPML can obtain the promising performance with a very low computational cost . Therefore , we presented a framework that uses unsupervised learning approach in order to perform precise identification of outliers for a given video frames with respect to the contextual information of the scene . The proposed system uses matrix decompostion method using multivariate analysis in order to maintain an equilibrium better faster response time and higher accuracy of the abnormal event/object detection as an outlier . Using an analytical methodology , the proposed system blocking operation followed by sparsity in order to perform detection . This makes it a cost-effective approach for the analysis of large videoendoscopy repositories . Other advantages of the proposed methodology include its capability to suggest possible locations of GI anomalies within the video frames , and its generality , in the sense that abnormal frame detection is based on automatically derived image features . It is implemented in three phases : 1 ) it classifies the video frames into abnormal or normal using a weakly supervised convolutional neural network ( WCNN ) architecture ; 2 ) detects salient points from deeper WCNN layers , using a deep saliency detection algorithm ; and 3 ) localizes GI anomalies using an iterative cluster unification ( ICU ) algorithm . Clustering-based approaches for abnormal video event detection have been proven to be effective in the recent literature . Based on the framework proposed in our previous work ( F. Jiang et al.,2007 ) , we have developed in this paper a new strategy for unsupervised trajectory clustering . This network allows us to not only extract robust spatiotemporal features but also ensures that all the target class representations are sufficiently compact to be effectively isolated from outliers . The proposed algorithm is evaluated on the challenging USCD Ped2 dataset for abnormal video event detection and localization . Both qualitative and quantitative experiments show competitive performances compared to the state-of-the-art methods . Intrusion detection systems need a mass of the labeled data in the process of training It hampers the application and popularity of traditional IDSs A study was conducted to realize the automation of the learning process of the detection models where the training data is unsupervised A novel method of unsupervised anomaly detection based on principal components analysis ( PCA ) is presented The main characteristics of the training samples are learned under the principle of least mean square errors The information of the samples is duplicated in the process of encoding and decoding The anomaly behaviors can be detected according to the anomaly factor defined by the square errors between the original vector and the resultant one The experiment of the simulation system proves that the method of unsupervised anomaly detection based on PCA does not need the participation of experts in the prophase The experimental result shows its effectiveness We evaluate their performance on Yahoo ! and Numenta dataset for unsupervised anomaly detection benchmark . To the best of authors ' knowledge , this is the first time a multi-scale streaming anomaly detection has been proposed and systematically studied . Index Terms — Unsupervised Anomaly Detection & Characterization , Robust & Distributed Clustering , Automatic Generation of Signatures , Autonomous Security . Identifying unusual or anomalous patterns in an underlying dataset is an important but challenging task in many applications . The focus of the unsupervised anomaly detection literature has mostly been on vectorised data . However , many applications are more naturally described using higher-order tensor representations . In Queensland , Australia , the Medicines Regulation and Quality Unit within the state health system maintains a database of prescribing events and uses this data to identify anomalies and provide subsequent support for patients and prescribers . In this study , we consider this task as an unsupervised anomaly detection problem . We use probability density estimation models to describe the distribution of the data over a number of key attributes and use the model to identify anomalies as points with low estimated probability . The rationale is to depart from model-based and simple rule-based techniques and adopt an approach , which detects anomalous events in an unsupervised manner . Further , we explore how incorporation of domain knowledge can assist the unsupervised anomaly detection process and we discuss practical issues . These shortcomings are addressed in this study , where 19 different unsupervised anomaly detection algorithms are evaluated on 10 different datasets from multiple application domains . By publishing the source code and the datasets , this paper aims to be a new well-funded basis for unsupervised anomaly detection research . Additionally , this evaluation reveals the strengths and weaknesses of the different approaches for the first time . Here , only normal data is required for training before anomalies can be detected . In theory , the one-class SVM could also be used in an unsupervised anomaly detection setup , where no prior training is conducted . Unfortunately , it turns out that a one-class SVM is sensitive to outliers in the data . Hubness is an angle for the increment of dimensionality relating to closest neighbors which has go to a consideration . This overview article , talks about some critical parts of the hubness in subtle element and presents a complete survey on the best in class specific calculations for unsupervised anomaly identification for high dimensional Data and part of hubness . Unsupervised visual anomaly detection is currently one of the most challenging machine learning problems . Various techniques have been specifically developed for certain limited domains while some are more generic towards a broader scope of applications . The value of data augmentation for learning invariance has been shown in Dosovitskiy et al . in the scope of unsupervised feature learning . Another challenge in many cell segmentation tasks is the separation of touching objects of the same class ; see [ reference ] . These latent representations encode social relations in a continuous vector space , which is easily exploited by statistical models . DeepWalk generalizes recent advancements in language modeling and unsupervised feature learning ( or deep learning ) from sequences of words to graphs . DeepWalk uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences . Of these , the appropriate decoders use the max - pooling indices received from the corresponding encoder to perform non - linear upsampling of their input feature maps . This idea was inspired from an architecture designed for unsupervised feature learning . Reusing max - pooling indices in the decoding process has several practical advantages ; ( i ) it improves boundary delineation , ( ii ) it reduces the number of parameters enabling end - to - end training , and ( iii ) this form of upsampling can be incorporated into any encoder - decoder architecture such as with only a little modification . to accept full image sizes as input to learn hierarchical encoders . Both these approaches however did not attempt to use deep encoder - decoder networks for unsupervised feature training as they discarded the decoders after each encoder training . Here , SegNet differs from these architectures as the deep encoder - decoder network is trained jointly for a supervised learning task and hence the decoders are an integral part of the network in test time . Striking examples include face recognition and visual question answering , where image features trained on ImageNet and word embeddings trained on large unsupervised corpora are combined . In contrast , most approaches for sentence representation learning are unsupervised , arguably because the NLP community has not yet found the best supervised task for embedding the semantics of a whole sentence . Another reason is that neural networks are very good at capturing the biases of the task on which they are trained , but can easily forget the overall information or semantics of the input data by specializing too much on these biases . Learning models on large unsupervised task makes it harder for the model to specialize . littwin2016multiverse showed that co - adaptation of encoders and classifiers , when trained end - to - end , can negatively impact the generalization power of image features generated by an encoder . By contrast , in our case we consider a single RGB image as input , based on which we establish a correspondence between surface points and image pixels . Several other works have recently aimed at recovering dense correspondences between pairs or sets of RGB images in an unsupervised setting . More recently , used the equivariance principle in order to align sets of images to a common coordinate system , while following the general idea of groupwise image alignment , e.g. . Previous work makes use of labeled data to factorize representations into class - related and class - independent components . Recently , the unsupervised setting has been explored . InfoGAN achieves disentanglement by maximizing the mutual information between latent variables and data variation . The method iteratively builds an ideally sparse target and optimizes the dictionary by minimizing the error between the system output and the ideally sparse target . Defining sparsity concepts in terms of expected output allows to exploit a new strategy in unsupervised training . It is worth stressing that many optimization strategies can be used to minimize the above - mentioned error and that parameters of these optimization techniques must not be considered as belonging to our approach . subsection : Imagenet - 1k We use Imagenet - 1k deng2009imagenet as a source of natural images for unsupervised training . We train on min - resized center crops . document : Invariant Information Clustering for Unsupervised Image Classification and Segmentation We present a novel clustering objective that learns a neural network classifier from scratch , given only unlabelled data samples . We tackle image super - resolution as a clustering - regression problem , and focus on the clustering stage , which happens at the split - nodes of each decision tree . By employing the JMPF strategy , we rotate the feature space into a pre - clustered feature space , which can cluster samples into different sub - spaces more compactly in an unsupervised problem . The compact pre - clustered feature space can provide the optimal thresholds for split - nodes in decision trees , which are the zero - centered orthogonal hyperplanes . A GAN framework with two discriminators appeared in [ reference ] , however , it is applicable only in a semi - supervised case where a label can be assigned to subsets of the dataset ( e.g. , X = { X 1 = Domain 1 , X 2 = Domain 2 , . . . } ) . In contrast , our framework applies to an unsupervised scenario where an obvious partition of the dataset is unknown . Furthermore , extending GMAN to the semi - supervised domain - adaptation scenario would suggest multiple discriminators per domain , therefore our line of research is strictly orthogonal to that of their multi - domain discriminator approach . paragraph : Benefits of fertility Fertilities possess all three of the properties listed earlier as desired of a latent variable for non - autoregressive machine translation : An external aligner provides a simple and fast approximate inference model that effectively reduces the unsupervised training problem to two supervised ones . Using fertilities as a latent variable makes significant progress towards solving the multimodality problem by providing a natural factorization of the output space . This gives the possibility to investigate aspects never considered so far , such as the tendency of a method of over- or under-segmenting groups , or of better dealing with specific group cardinalities . The metrics have been applied to all the publicly available approaches of group detection , discovering interesting strength and pitfalls of the current literature , and promoting further research in the field . To show the effectiveness of proposed method , we evaluated the method through a series of experiments and investigated the effect on group detection by the difference of human proximity . As the results , we found that the group detection was affected by the difference of human proximity . An advantage of the method is that we do not consider any particular form for the probability measures . We validate our method in the task of group anomaly detection , with artificial and real datasets . FGM is designed to characterize data groups at both the point level and the group level so as to detect various types of group anomalies . We evaluate the effectiveness of FGM on both synthetic and real data sets including images and turbulence data , and show that it is superior to existing approaches in detecting group anomalies . We conduct extensive experiments to evaluate our models on real world datasets . The empirical results demonstrate that our approach is effective and robust in detecting group anomalies . Code related to this paper is available at : https://github.com/raghavchalapathy/gad , https://www.cs.cmu.edu/~lxiong/gad/gad.html , https://github.com/jorjasso/SMDD-group-anomaly-detection , https://github.com/cjlin1/libsvm . Further , due to the unavoidable false alarms generated by a single sensor in a network , many deployed systems use group based detection to reduce system level false alarms . Despite the popularity of group based detection , few analysis works in the literature deal with group based detection . In this paper , we propose a novel approach called Markov chain based Spatial approach ( MS-approach ) to model group based detection in sensor networks . Samples may only be weakly atypical individually , whereas they may be strongly atypical when considered jointly . What makes this group anomaly detection problem quite challenging is that it is a priori unknown which subset of features jointly manifests a particular group of anomalies . Moreover , it is unknown how many anomalous groups are present in a given data batch . To transform the traditional CRM methods from individuals to social groups , we propose a novel technical framework ( GCRM ) to manage the social groups in massive telecom call graphs . Our framework is based on a series of newly emerged methods for social network analysis , such as group detecting , group evolution tracking and group life-cycle modeling in telecom applications . We analyze the relationships between social groups and propose a method to find potential customers in these groups . The proposed group detection method is anchored on the Latent Dirichlet Allocation ( LDA ) topic modeling technique , aiming at identifying clusters of twitter users that are optimal in terms of both spatial and topical compactness . Specifically , the group detection problem is formulated as a multi-objective optimization problem taking into consideration two complementary cluster formation directives . The first objective , related to spatial compactness , is achieved by minimizing the overall deviation from the corresponding cluster centers . Existing methods of point anomaly detection are ineffective for scenarios where individual instances are normal , but their collective behavior as a group is abnormal . In this paper , we formalize this group anomaly detection issue , and propose a novel non-parametric bayesian model , named Multi-view Group Anomaly Detection ( MGAD ) . By representing the multi-view data with different latent group and topic structures , MGAD first discovers the distribution of groups or topics in each view , then detects group anomalies effectively . We evaluate our proposed method for different tasks , including classification , detection , and segmentation based on the learned latent representations . In addition , we adopt the auto-encoding capability of our proposed method for applications like defense against adversarial example attacks and video anomaly detection . Results confirm the performance of our method is better or at least comparable with the state-of-the-art for each specific application , but with a generic and self-supervised approach . Using our OCC , we first test outlier detection on two image data sets , Modified National Institute of Standards and Technology ( MNIST ) and Caltech-256 . Then , several experiments for video anomaly detection are performed on University of Minnesota ( UMN ) and University of California , San Diego ( UCSD ) data sets . Our proposed method can successfully learn the target class underlying distribution and outperforms other approaches . This is , however , a challenging problem due to the growing collection of data without the knowledge of what to be defined as “ abnormal ” , and the expensive feature engineering procedure . In this paper we introduce a unified framework for anomaly detection in video based on the restricted Boltzmann machine ( \(\text {RBM}\ ) ) , a recent powerful method for unsupervised learning and representation learning . Our proposed system works directly on the image pixels rather than hand-crafted features , it learns new representations for data in a completely unsupervised manner without the need for labels , and then reconstructs the data to recognize the locations of abnormal events based on the reconstruction errors . It allows the visual and interactive transmission of information related to the physical behavior of the elements , defined as a function of the time variable . To this end , the basic knowledge of material most often used in walls , anomaly surveillance , techniques of rehabilitation , and inspection planning were studied . This information was included in a database that supports the periodic inspection needed in a program of preventive maintenance . It allows the visual and interactive transmission of information related to the physical behavior of the elements . To this end , the basic knowledge of material most often used in facades , anomaly surveillance , techniques of rehabilitation , and inspection planning were studied . This information was included in a database that supports the periodic inspection needed in a program of preventive maintenance . Camera anomaly detection , an important part of modern digital security and surveillance cameras , captures malicious actions against the camera like spray paint , occlusion , and displacement . Multiple challenges exist for solving camera anomaly detection in a practical way , such as limited computational budget and difficult tampering events . In this article , we present a fast and accurate approach for camera anomaly detection , which consists of the application of low-level computer vision techniques combined with a unique automatic parameter calibration method that eliminates the need for manual parameter tuning . Based on the results of our experiments , the proposed algorithm detects more than 96 percent of malicious events without false alarms for 13 hours of video with 350 tampering events from more than 20 different scenes . The aim of surveillance video anomaly detection is to detect events that rarely or never happened in a specified scene . Different detectors can detect different anomalies . Experimental results demonstrate that the proposed hybrid optimization based on genetic algorithms can quickly obtain the optimal feature subset and SVM parameters . Therefore the proposed scheme reduces time and improves the accuracy of surveillance video anomaly detection . However , this is very challenging due to large variations in environment and human movement , and also due to the vague definition of anomaly in the domain of video surveillance . In this paper , we would like to give informational insights on how techniques from the four domains above can be applied to perform video-based anomaly detection . This review article surveys the current progresses made toward video-based anomaly detection . We address the most fundamental aspect for video anomaly detection , that is , video feature representation . In this work , we propose several enhancements to a geometric transformation based model for anomaly detection in images ( GeoTranform ) . The model assumes that the anomaly class is unknown and that only inlier samples are available for training . Though many prominent datasets are available , there is lack of description and categorization of these datasets according to the application demand . A survey on anomaly detection video datasets is necessary for researchers to understand and progress in this domain . This paper presents various datasets description briefly . This allows the detection of potential intruders even in front of trees and grass waving in the wind , waves across a lake , or cars moving past . In this paper we present a general framework for the identification of anomalies in video , and a comparison of statistical models that characterize the local video dynamics at each pixel neighborhood . A real-time implementation of these algorithms runs on an 800 MHz laptop , and we present qualitative results in many application domains . The proposed unsupervised sound detection method which does not require any pretrained models is based on the use of the exponential family model and 1-SVMs to approximate the generalized likelihood ratio . Then , we apply novel discriminative algorithms based on 1-SVMs with new dissimilarity measure in order to address a supervised sound-classification task . We compare the novel sound detection and classification methods with other popular approaches . We apply these constraints to sample training data for triplet-loss embedding models using a large unlabeled dataset of YouTube soundtracks . The resulting low-dimensional representations provide both greatly improved query-by-example retrieval performance and reduced labeled data and model complexity requirements for supervised sound classification . Furthermore , the system will have the ability to generate reports and make a copy of the audio , the anomaly recorded . In the present project is to develop a sound anomaly detection indoors optimizing resources such as process data acquisition and efficient algorithms . Use of an autoencoder ( AE ) as a normal model is a state-of-the-art technique for unsupervised-anomaly detection in sounds ( ADS ) . The AE is trained to minimize the sample mean of the anomaly score of normal sounds in a mini-batch . Since all the different sounds in a surveillance environment can not be anticipated , a surveillance system for event detection can not completely rely on a supervised audio classification framework . In this paper , we propose a hybrid solution that consists two parts ; one that performs unsupervised audio analysis and another that performs analysis using an audio classification framework obtained from off-line analysis and training . The proposed system is capable of detecting new kinds of suspicious audio events that occur as outliers against a background of usual activity .
The proposed JMIM method outperforms five competing methods in terms of accuracy . Feature selection is used in many application areas relevant to expert and intelligent systems , such as data mining and machine learning , image processing , anomaly detection , bioinformatics and natural language processing . Feature selection based on information theory is a popular approach due its computational efficiency , scalability in terms of the dataset dimensionality , and independence from the classifier .
First , spectral , extend morphological profiles ( EMPs ) , and Gabor texture features are captured from an input HSI . Then , we define the anomaly detection problem as an anomaly game model , in which image regions ( superpixels ) of different features are modeled as players who select to be “ anomaly ” or “ background ” as their strategies . Three initial detection results are produced based on each player ’s strategy in the Nash equilibrium of the anomaly game .
Keywords : Intrusion Detection , Anomaly-based Detection , Signature-based detection
Intrusion detection systems ( IDS ) raise alerts if any unusual network traffic is detected and thus remains critical for network safety . Utilization of machine learning led methodologies in anomaly-based detection has gained popularity in recent years . In this paper , we conduct a thorough survey of studies based on the intrusion detection systems by using machine learning algorithms and present comparison dependent on dataset used , data reduction approaches , type of classifiers used and the outcome achieved by such different algorithms .
In recent years , medical wireless sensor networks suffer from several anomalies and defaults , which can cause the malfunctioning of the whole network , many anomaly detection mechanisms , have been proposed . Therefore , the anomaly detection process is a required function in the medical environment to help doctors in making appropriate and accurate decisions . We propose in this paper to enhance the anomaly detection algorithm by adding a correlation function using data from different body sensor types .
Confident decisions upon the abnormality of a particular vessel behavior require a certain level of situation awareness that may be difficult to achieve when the operator is overloaded by the available information . Based on a visual analytics process model , we present a novel system that supports the acquisition of situation awareness and the involvement of the user in the anomaly detection process using two layers of interactive visualizations . The system uses an interactive data mining module that supports the insertion of the user 's knowledge and experience in the creation , validation and continuous update of the normal model of the environment .
The efficient of detection is improved awfully . The model of anomaly detecting is used the technique of artificial immune . Our system worked well at detecting both known and new attacks .
The K-th moment filter has the advantage of lower computational complexity and is more effective in on-line measurement system . On the other hand , the minimum entropy filter can remain more local tiny features in the processed signals by adjusting the parameters properly so that is better in anomaly detecting of the defects in different levels .
Bayesian networks can be regarded as a powerful tool for macro programming a spatial computer , such as a dense sensor network , in a variety of data analysis applications . In this article we present our architecture to program a spatial computer by means of a distributed Bayesian network and present some applications we developed over a sensor network testing both inference and anomaly-detection analysis .
Anomaly detection and distinction are in the thick of major challenges that network managers encounter . Complication of monitoring the broaden and complex WLANs , that often requires heavy instrumentation of the user devices , makes the anomaly detection analysis even harder . In this paper we exploit 802.11 access point usage data and propose an anomaly detection technique based on Hidden Markov Model ( HMM ) and Universal Background Model ( UBM ) on data that is inexpensive to obtain .
It is critical for a reliable cloud to effectively find out root causes of the cloud service anomalies for efficacious treatment . System logs are widely used for anomaly detection and analysis . Many efforts have been made to handle massive cloud logs automatically .
In order to deal with the unknown attack of the cloud platform and distinguish the behavior after the attack from the peak load under normal work , this paper studies a cloud data center anomaly detection method for Distributed Denial of Service(DDoS ) attacks . We built a virtual machine cluster on openstack , simulated DDoS attacks on virtual machines , collected CPU utilization and network traffic data before and after the attack , and performed anomaly detection and analysis through a single class classification algorithm and analysis of time window sequences . We evaluated the performance of this method through lab-based experiments and real-world cloud data center experiments .
( 4 ) The experiment result shows that MC-CNN has the highest accuracy compared with conventional machine learning methods . The result also proves that our approach has performed well not only in DDoS attack detection but also in other anomaly attack detection .
There are described essential aspects of threats to safety of correct operation of a WSN network with regard to possible attack types . A two-stage method of anomaly/attack detection was proposed . Stage One consisted in finding and eliminating of any outlying observations in the analyzed parameters of the WSN network traffic by means of Cook ’s distance .
For our study , we annotated and analyzed a new human pose dataset from a clinical setting to study the subject-wise sensitivity and accuracy of anomaly detection on our proposed variational autoencoder ( VAEs ) } based frameworks . For our study , we performed anomaly analysis and detection based on our frameworks with PatientPose , a 2D pose estimator designed for the clinic setting . We demonstrate a strategy to correct anomalous to improve pose estimation accuracy and quantify and consider design-tradeoffs for our anomalous pose detection method .
It is an online learning algorithm which builds useful incremental results , and is trivially parallelizable . These qualities make it suitable for a broad class of real world applications such as network classification , and anomaly detection . section : INTRODUCTION
Intrusion detection systems play an important role in numerous industrial applications , such as network security and abnormal event detection . They effectively protect our critical computer systems or networks against the network attackers .
Automated visual surveillance systems are attracting extensive interest due to public security . In this paper , we attempt to mine semantic context information including object-specific context information and scene-specific context information ( learned from object-specific context information ) to build an intelligent system with robust object detection , tracking , and classification and abnormal event detection . By means of object-specific context information , a cotrained classifier , which takes advantage of the multiview information of objects and reduces the number of labeling training samples , is learned to classify objects into pedestrians or vehicles with high object classification performance .
In the sparse online scheme , the model complexity is controlled by the coherence criterion . The online LS-OC-SVM is adopted to handle the abnormal event detection problem . Each frame of the video is characterized by the covariance matrix descriptor encoding the moving information , then is classified into a normal or an abnormal frame .
This paper proposes a novel method to detect suspicious objects from videos for abnormal event analysis . When considering a robbery event happens , there should be some suspicious object transferring conditions following between the forager and the victim .
After that , a spider-web structure is proposed for missing object detection even though there are large camera movements between any two adjacent frames . Experimental results prove that our proposed system is efficient , robust , and superior in missing object detection and abnormal event analysis .
To detect abnormal events in crowded environment , low-level features of moving objects are extracted from video frames in optical-flow form . These features are used to get motion correlation model which is used in abnormal event detection phase . There are two steps in abnormal event detection phase . The first detection step is based on local model which is made with local information such as velocities , directions and positions and the second step is based on gobal model which is made with global information .
We present a computational approach to abnormal visual event detection , which is based on exploring and modeling local motion patterns in a non-linear subspace . We use motion vectors extracted over a region of interest ( ROI ) as features and a non-linear , graph-based manifold learning algorithm coupled with a supervised novelty classifier to label segments of a video sequence .
In addition , an extension of OPML ( namely COPML ) is further proposed to enhance the robustness when in real case the first several samples come from the same class ( i.e. , cold start problem ) . In the experiments , we have systematically evaluated our methods ( OPML and COPML ) on three typical tasks , including UCI data classification , face verification , and abnormal event detection in videos , which aims to fully evaluate the proposed methods on different sample number , different feature dimensionalities and different feature extraction ways ( i.e. , hand-crafted and deeply-learned ) . The results show that OPML and COPML can obtain the promising performance with a very low computational cost .
Therefore , we presented a framework that uses unsupervised learning approach in order to perform precise identification of outliers for a given video frames with respect to the contextual information of the scene . The proposed system uses matrix decompostion method using multivariate analysis in order to maintain an equilibrium better faster response time and higher accuracy of the abnormal event/object detection as an outlier . Using an analytical methodology , the proposed system blocking operation followed by sparsity in order to perform detection .
This makes it a cost-effective approach for the analysis of large videoendoscopy repositories . Other advantages of the proposed methodology include its capability to suggest possible locations of GI anomalies within the video frames , and its generality , in the sense that abnormal frame detection is based on automatically derived image features . It is implemented in three phases : 1 ) it classifies the video frames into abnormal or normal using a weakly supervised convolutional neural network ( WCNN ) architecture ; 2 ) detects salient points from deeper WCNN layers , using a deep saliency detection algorithm ; and 3 ) localizes GI anomalies using an iterative cluster unification ( ICU ) algorithm .
Clustering-based approaches for abnormal video event detection have been proven to be effective in the recent literature . Based on the framework proposed in our previous work ( F. Jiang et al.,2007 ) , we have developed in this paper a new strategy for unsupervised trajectory clustering .
This network allows us to not only extract robust spatiotemporal features but also ensures that all the target class representations are sufficiently compact to be effectively isolated from outliers . The proposed algorithm is evaluated on the challenging USCD Ped2 dataset for abnormal video event detection and localization . Both qualitative and quantitative experiments show competitive performances compared to the state-of-the-art methods .
Intrusion detection systems need a mass of the labeled data in the process of training It hampers the application and popularity of traditional IDSs A study was conducted to realize the automation of the learning process of the detection models where the training data is unsupervised A novel method of unsupervised anomaly detection based on principal components analysis ( PCA ) is presented The main characteristics of the training samples are learned under the principle of least mean square errors The information of the samples is duplicated in the process of encoding and decoding The anomaly behaviors can be detected according to the anomaly factor defined by the square errors between the original vector and the resultant one The experiment of the simulation system proves that the method of unsupervised anomaly detection based on PCA does not need the participation of experts in the prophase The experimental result shows its effectiveness
We evaluate their performance on Yahoo ! and Numenta dataset for unsupervised anomaly detection benchmark . To the best of authors ' knowledge , this is the first time a multi-scale streaming anomaly detection has been proposed and systematically studied .
Index Terms — Unsupervised Anomaly Detection & Characterization , Robust & Distributed Clustering , Automatic Generation of Signatures , Autonomous Security .
Identifying unusual or anomalous patterns in an underlying dataset is an important but challenging task in many applications . The focus of the unsupervised anomaly detection literature has mostly been on vectorised data . However , many applications are more naturally described using higher-order tensor representations .
In Queensland , Australia , the Medicines Regulation and Quality Unit within the state health system maintains a database of prescribing events and uses this data to identify anomalies and provide subsequent support for patients and prescribers . In this study , we consider this task as an unsupervised anomaly detection problem . We use probability density estimation models to describe the distribution of the data over a number of key attributes and use the model to identify anomalies as points with low estimated probability .
The rationale is to depart from model-based and simple rule-based techniques and adopt an approach , which detects anomalous events in an unsupervised manner . Further , we explore how incorporation of domain knowledge can assist the unsupervised anomaly detection process and we discuss practical issues .
These shortcomings are addressed in this study , where 19 different unsupervised anomaly detection algorithms are evaluated on 10 different datasets from multiple application domains . By publishing the source code and the datasets , this paper aims to be a new well-funded basis for unsupervised anomaly detection research . Additionally , this evaluation reveals the strengths and weaknesses of the different approaches for the first time .
Here , only normal data is required for training before anomalies can be detected . In theory , the one-class SVM could also be used in an unsupervised anomaly detection setup , where no prior training is conducted . Unfortunately , it turns out that a one-class SVM is sensitive to outliers in the data .
Hubness is an angle for the increment of dimensionality relating to closest neighbors which has go to a consideration . This overview article , talks about some critical parts of the hubness in subtle element and presents a complete survey on the best in class specific calculations for unsupervised anomaly identification for high dimensional Data and part of hubness .
Unsupervised visual anomaly detection is currently one of the most challenging machine learning problems . Various techniques have been specifically developed for certain limited domains while some are more generic towards a broader scope of applications .
The value of data augmentation for learning invariance has been shown in Dosovitskiy et al . in the scope of unsupervised feature learning . Another challenge in many cell segmentation tasks is the separation of touching objects of the same class ; see [ reference ] .
These latent representations encode social relations in a continuous vector space , which is easily exploited by statistical models . DeepWalk generalizes recent advancements in language modeling and unsupervised feature learning ( or deep learning ) from sequences of words to graphs . DeepWalk uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences .
Of these , the appropriate decoders use the max - pooling indices received from the corresponding encoder to perform non - linear upsampling of their input feature maps . This idea was inspired from an architecture designed for unsupervised feature learning . Reusing max - pooling indices in the decoding process has several practical advantages ; ( i ) it improves boundary delineation , ( ii ) it reduces the number of parameters enabling end - to - end training , and ( iii ) this form of upsampling can be incorporated into any encoder - decoder architecture such as with only a little modification .
to accept full image sizes as input to learn hierarchical encoders . Both these approaches however did not attempt to use deep encoder - decoder networks for unsupervised feature training as they discarded the decoders after each encoder training . Here , SegNet differs from these architectures as the deep encoder - decoder network is trained jointly for a supervised learning task and hence the decoders are an integral part of the network in test time .
Striking examples include face recognition and visual question answering , where image features trained on ImageNet and word embeddings trained on large unsupervised corpora are combined . In contrast , most approaches for sentence representation learning are unsupervised , arguably because the NLP community has not yet found the best supervised task for embedding the semantics of a whole sentence . Another reason is that neural networks are very good at capturing the biases of the task on which they are trained , but can easily forget the overall information or semantics of the input data by specializing too much on these biases . Learning models on large unsupervised task makes it harder for the model to specialize . littwin2016multiverse showed that co - adaptation of encoders and classifiers , when trained end - to - end , can negatively impact the generalization power of image features generated by an encoder .
By contrast , in our case we consider a single RGB image as input , based on which we establish a correspondence between surface points and image pixels . Several other works have recently aimed at recovering dense correspondences between pairs or sets of RGB images in an unsupervised setting . More recently , used the equivariance principle in order to align sets of images to a common coordinate system , while following the general idea of groupwise image alignment , e.g. .
Previous work makes use of labeled data to factorize representations into class - related and class - independent components . Recently , the unsupervised setting has been explored . InfoGAN achieves disentanglement by maximizing the mutual information between latent variables and data variation .
The method iteratively builds an ideally sparse target and optimizes the dictionary by minimizing the error between the system output and the ideally sparse target . Defining sparsity concepts in terms of expected output allows to exploit a new strategy in unsupervised training . It is worth stressing that many optimization strategies can be used to minimize the above - mentioned error and that parameters of these optimization techniques must not be considered as belonging to our approach .
subsection : Imagenet - 1k We use Imagenet - 1k deng2009imagenet as a source of natural images for unsupervised training . We train on min - resized center crops .
document : Invariant Information Clustering for Unsupervised Image Classification and Segmentation We present a novel clustering objective that learns a neural network classifier from scratch , given only unlabelled data samples .
We tackle image super - resolution as a clustering - regression problem , and focus on the clustering stage , which happens at the split - nodes of each decision tree . By employing the JMPF strategy , we rotate the feature space into a pre - clustered feature space , which can cluster samples into different sub - spaces more compactly in an unsupervised problem . The compact pre - clustered feature space can provide the optimal thresholds for split - nodes in decision trees , which are the zero - centered orthogonal hyperplanes .
A GAN framework with two discriminators appeared in [ reference ] , however , it is applicable only in a semi - supervised case where a label can be assigned to subsets of the dataset ( e.g. , X = { X 1 = Domain 1 , X 2 = Domain 2 , . . . } ) . In contrast , our framework applies to an unsupervised scenario where an obvious partition of the dataset is unknown . Furthermore , extending GMAN to the semi - supervised domain - adaptation scenario would suggest multiple discriminators per domain , therefore our line of research is strictly orthogonal to that of their multi - domain discriminator approach .
paragraph : Benefits of fertility Fertilities possess all three of the properties listed earlier as desired of a latent variable for non - autoregressive machine translation : An external aligner provides a simple and fast approximate inference model that effectively reduces the unsupervised training problem to two supervised ones . Using fertilities as a latent variable makes significant progress towards solving the multimodality problem by providing a natural factorization of the output space .
This gives the possibility to investigate aspects never considered so far , such as the tendency of a method of over- or under-segmenting groups , or of better dealing with specific group cardinalities . The metrics have been applied to all the publicly available approaches of group detection , discovering interesting strength and pitfalls of the current literature , and promoting further research in the field .
To show the effectiveness of proposed method , we evaluated the method through a series of experiments and investigated the effect on group detection by the difference of human proximity . As the results , we found that the group detection was affected by the difference of human proximity .
An advantage of the method is that we do not consider any particular form for the probability measures . We validate our method in the task of group anomaly detection , with artificial and real datasets .
FGM is designed to characterize data groups at both the point level and the group level so as to detect various types of group anomalies . We evaluate the effectiveness of FGM on both synthetic and real data sets including images and turbulence data , and show that it is superior to existing approaches in detecting group anomalies .
We conduct extensive experiments to evaluate our models on real world datasets . The empirical results demonstrate that our approach is effective and robust in detecting group anomalies . Code related to this paper is available at : https://github.com/raghavchalapathy/gad , https://www.cs.cmu.edu/~lxiong/gad/gad.html , https://github.com/jorjasso/SMDD-group-anomaly-detection , https://github.com/cjlin1/libsvm .
Further , due to the unavoidable false alarms generated by a single sensor in a network , many deployed systems use group based detection to reduce system level false alarms . Despite the popularity of group based detection , few analysis works in the literature deal with group based detection . In this paper , we propose a novel approach called Markov chain based Spatial approach ( MS-approach ) to model group based detection in sensor networks .
Samples may only be weakly atypical individually , whereas they may be strongly atypical when considered jointly . What makes this group anomaly detection problem quite challenging is that it is a priori unknown which subset of features jointly manifests a particular group of anomalies . Moreover , it is unknown how many anomalous groups are present in a given data batch .
To transform the traditional CRM methods from individuals to social groups , we propose a novel technical framework ( GCRM ) to manage the social groups in massive telecom call graphs . Our framework is based on a series of newly emerged methods for social network analysis , such as group detecting , group evolution tracking and group life-cycle modeling in telecom applications . We analyze the relationships between social groups and propose a method to find potential customers in these groups .
The proposed group detection method is anchored on the Latent Dirichlet Allocation ( LDA ) topic modeling technique , aiming at identifying clusters of twitter users that are optimal in terms of both spatial and topical compactness . Specifically , the group detection problem is formulated as a multi-objective optimization problem taking into consideration two complementary cluster formation directives . The first objective , related to spatial compactness , is achieved by minimizing the overall deviation from the corresponding cluster centers .
Existing methods of point anomaly detection are ineffective for scenarios where individual instances are normal , but their collective behavior as a group is abnormal . In this paper , we formalize this group anomaly detection issue , and propose a novel non-parametric bayesian model , named Multi-view Group Anomaly Detection ( MGAD ) . By representing the multi-view data with different latent group and topic structures , MGAD first discovers the distribution of groups or topics in each view , then detects group anomalies effectively .
We evaluate our proposed method for different tasks , including classification , detection , and segmentation based on the learned latent representations . In addition , we adopt the auto-encoding capability of our proposed method for applications like defense against adversarial example attacks and video anomaly detection . Results confirm the performance of our method is better or at least comparable with the state-of-the-art for each specific application , but with a generic and self-supervised approach .
Using our OCC , we first test outlier detection on two image data sets , Modified National Institute of Standards and Technology ( MNIST ) and Caltech-256 . Then , several experiments for video anomaly detection are performed on University of Minnesota ( UMN ) and University of California , San Diego ( UCSD ) data sets . Our proposed method can successfully learn the target class underlying distribution and outperforms other approaches .
This is , however , a challenging problem due to the growing collection of data without the knowledge of what to be defined as “ abnormal ” , and the expensive feature engineering procedure . In this paper we introduce a unified framework for anomaly detection in video based on the restricted Boltzmann machine ( \(\text {RBM}\ ) ) , a recent powerful method for unsupervised learning and representation learning . Our proposed system works directly on the image pixels rather than hand-crafted features , it learns new representations for data in a completely unsupervised manner without the need for labels , and then reconstructs the data to recognize the locations of abnormal events based on the reconstruction errors .
It allows the visual and interactive transmission of information related to the physical behavior of the elements , defined as a function of the time variable . To this end , the basic knowledge of material most often used in walls , anomaly surveillance , techniques of rehabilitation , and inspection planning were studied . This information was included in a database that supports the periodic inspection needed in a program of preventive maintenance .
It allows the visual and interactive transmission of information related to the physical behavior of the elements . To this end , the basic knowledge of material most often used in facades , anomaly surveillance , techniques of rehabilitation , and inspection planning were studied . This information was included in a database that supports the periodic inspection needed in a program of preventive maintenance .
Camera anomaly detection , an important part of modern digital security and surveillance cameras , captures malicious actions against the camera like spray paint , occlusion , and displacement . Multiple challenges exist for solving camera anomaly detection in a practical way , such as limited computational budget and difficult tampering events . In this article , we present a fast and accurate approach for camera anomaly detection , which consists of the application of low-level computer vision techniques combined with a unique automatic parameter calibration method that eliminates the need for manual parameter tuning . Based on the results of our experiments , the proposed algorithm detects more than 96 percent of malicious events without false alarms for 13 hours of video with 350 tampering events from more than 20 different scenes .
The aim of surveillance video anomaly detection is to detect events that rarely or never happened in a specified scene . Different detectors can detect different anomalies .
Experimental results demonstrate that the proposed hybrid optimization based on genetic algorithms can quickly obtain the optimal feature subset and SVM parameters . Therefore the proposed scheme reduces time and improves the accuracy of surveillance video anomaly detection .
However , this is very challenging due to large variations in environment and human movement , and also due to the vague definition of anomaly in the domain of video surveillance . In this paper , we would like to give informational insights on how techniques from the four domains above can be applied to perform video-based anomaly detection .
This review article surveys the current progresses made toward video-based anomaly detection . We address the most fundamental aspect for video anomaly detection , that is , video feature representation .
In this work , we propose several enhancements to a geometric transformation based model for anomaly detection in images ( GeoTranform ) . The model assumes that the anomaly class is unknown and that only inlier samples are available for training .
Though many prominent datasets are available , there is lack of description and categorization of these datasets according to the application demand . A survey on anomaly detection video datasets is necessary for researchers to understand and progress in this domain . This paper presents various datasets description briefly .
This allows the detection of potential intruders even in front of trees and grass waving in the wind , waves across a lake , or cars moving past . In this paper we present a general framework for the identification of anomalies in video , and a comparison of statistical models that characterize the local video dynamics at each pixel neighborhood . A real-time implementation of these algorithms runs on an 800 MHz laptop , and we present qualitative results in many application domains .
The proposed unsupervised sound detection method which does not require any pretrained models is based on the use of the exponential family model and 1-SVMs to approximate the generalized likelihood ratio . Then , we apply novel discriminative algorithms based on 1-SVMs with new dissimilarity measure in order to address a supervised sound-classification task . We compare the novel sound detection and classification methods with other popular approaches .
We apply these constraints to sample training data for triplet-loss embedding models using a large unlabeled dataset of YouTube soundtracks . The resulting low-dimensional representations provide both greatly improved query-by-example retrieval performance and reduced labeled data and model complexity requirements for supervised sound classification .
Furthermore , the system will have the ability to generate reports and make a copy of the audio , the anomaly recorded . In the present project is to develop a sound anomaly detection indoors optimizing resources such as process data acquisition and efficient algorithms .
Use of an autoencoder ( AE ) as a normal model is a state-of-the-art technique for unsupervised-anomaly detection in sounds ( ADS ) . The AE is trained to minimize the sample mean of the anomaly score of normal sounds in a mini-batch .
Since all the different sounds in a surveillance environment can not be anticipated , a surveillance system for event detection can not completely rely on a supervised audio classification framework . In this paper , we propose a hybrid solution that consists two parts ; one that performs unsupervised audio analysis and another that performs analysis using an audio classification framework obtained from off-line analysis and training . The proposed system is capable of detecting new kinds of suspicious audio events that occur as outliers against a background of usual activity .
Feature selection using Joint Mutual Information Maximisation Game Theory-Based Hyperspectral Anomaly Detection Improved Intrusion Detection System Using Discriminative learning Approach (A Review) Machine Learning Approach to IDS: A Comprehensive Review Correlation for efficient anomaly detection in medical environment Visual Analytics for the Detection of Anomalous Maritime Behavior Intrusion detection system based on anomaly and misuse Yarn Anomaly Detection Using K-th Moment and Entropy Filters Macro Programming a Spatial Computer with Bayesian Networks 802.11 Wireless Simulation and Anomaly Detection using HMM and UBM Learning-Based Anomaly Cause Tracing with Synthetic Analysis of Logs from Multiple Cloud Service Components CPU and network traffic anomaly detection method for cloud data center DAD-MCNN: DDoS Attack Detection via Multi-channel CNN Dynamic Time Warping Analysis for Security Purposes in Wireless Sensor Networks A Study on Human Pose Data Anomaly Detection DeepWalk: online learning of social representations Discovering Graphical Visual Features for Abnormal Semantic Event Detection Mining Semantic Context Information for Intelligent Video Surveillance of Traffic Scenes Online Least Squares One-Class Support Vector Machines-Based Abnormal Visual Event Detection Suspicious object detection using fuzzy-color histogram Abnormal Event Analysis Using Patching Matching and Concentric Features Correlation based Abnormal Motion Detection In Crowded Environment Event monitoring via local motion abnormality detection in non-linear subspace OPML: A one-pass closed-form solution for online metric learning Framework for Contextual Outlier Identification using Multivariate Analysis approach and Unsupervised Learning Detecting and Locating Gastrointestinal Anomalies Using Deep Learning and Iterative Cluster Unification Abnormal event detection based on trajectory clustering by 2-depth greedy search Enhanced Convolutional Neural Network for Abnormal Event Detection in Video Streams Unsupervised Anomaly Detection Based on Principal Components Analysis Multi-scale streaming anomalies detection for time series AUTONOMOUS NETWORK SECURITY FOR DETECTION OF NETWORK ATTACKS R1STM: One-class Support Tensor Machine with Randomised Kernel Detecting Anomalies in Controlled Drug Prescription Data Using Probabilistic Models Detecting Anomalous Behavior Towards Predictive Maintenance A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data Enhancing one-class support vector machines for unsupervised anomaly detection Invalid Detachment centered Outlier Recognition acquired from Reverse Nearest Neighbors Ensemble Grid Formation to Detect Potential Anomalous Regions Using Context Encoders U-Net: Convolutional Networks for Biomedical Image Segmentation DeepWalk: online learning of social representations SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Supervised Learning of Universal Sentence Representations from Natural Language Inference Data DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild Diverse Image-to-Image Translation via Disentangled Representations No more meta-parameter tuning in unsupervised sparse feature learning Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Invariant Information Clustering for Unsupervised Image Classification and Segmentation Joint maximum purity forest with application to image super-resolution Generative Multi-Adversarial Networks Non-Autoregressive Neural Machine Translation The GRODE metrics: Exploring the performance of group detection approaches Effect on Group Detection Based on Human Proximity for Human Relationship Extraction in Daily Life Support Measure Data Description for group anomaly detection Group Anomaly Detection using Flexible Genre Models Group Anomaly Detection using Deep Generative Models Performance Analysis of Group Based Detection for Sparse Sensor Networks Detecting clusters of anomalies on low-dimensional feature subsets with application to network traffic flow data Group CRM: a new telecom CRM framework from social network perspective Identifying semantically meaningful sub-communities within Twitter blogosphere Multi-View Group Anomaly Detection Self-Supervised Representation Learning via Neighborhood-Relational Encoding Deep End-to-End One-Class Classifier. Energy-Based Localized Anomaly Detection in Video Surveillance Virtual Reality Technology used to Support the Buildings Inspection Activity Virtual Environment in Construction and Maintenance of Buildings Efficient Camera Tampering Detection with Automatic Parameter Calibration Master-Auxiliary: an efficient aggregation strategy for video anomaly detection Abnormal event detection based on SVM in video surveillance Modeling video-based anomaly detection using deep architectures: Challenges and possibilities Modeling Representation of Videos for Anomaly Detection using Deep Learning: A Review Transformation Based Deep Anomaly Detection in Astronomical Images A survey of video datasets for anomaly detection in automated surveillance Spatio-temporal Background Models for Outdoor Surveillance One-Class SVMs Challenges in Audio Detection and Classification Applications Towards Learning Semantic Audio Representations from Unlabeled Data Detección de anomalías sonoras aplicado a la seguridad en ambientes cerrados usando matlab Batch Uniformization for Minimizing Maximum Anomaly Score of Dnn-Based Anomaly Detection in Sounds Audio analysis for surveillance applications
412ccba130dddf63dea07e6b8f5686fd756acd63 3500b314ae6b3813dfe3b3aa2626be94968ca2e9 335e3737e9cb1aba7b61c40a79bf9da73e5802cc e059d46ce443d0c30d06fd907393e9231dbee568 720b335207fa6819fc2f618bf127622308fa5e73 f2444054466798f5616ded937b65d8519ac6b6dd a8f296b3f2cf1914e29878aa68cbcbf3f926142f 9f2264e25b2cb40b03c1a8ea980f97f1052d0c0b 2ad58ec59fa38b117f8c7bde2367c17d8bab9a5a 4a3caf0138672f2dd147384f6b774646ddd9ec21 22f898b32015c0d5617f78a134a2157224587bf2 576b1b922676741cb408a5376fce68a0dfe58126 530f897560f8a84b50c8eb85b1c1eb607bd027bc 910dfa88e49fce4ac0b00ef03607bb3acd4d50a0 8af595b0e9ec3234f712f5076ce29857ee067568 327167ab51e001092bbfc2bbac219bb2c2a1ea39 a281f3025a9b25f6c1753e8f1c06bd327907c0dd fa757394cc897e60e532553f590f1c15b6acf5f8 dad16552bb146847d8ff74b9332e7673b230dc48 a9f11116170626ee5dfbf62ddc936c174fad1e4c 5ad1fa6e0c9c8534a583843bb5d6419251a7addc f446d9b43cfd3d7c6ca5a373a2259b01fa9850e8 878b53c6fb66b343e901200e8a88860a2a93d5f2 9474d6c12f528c8e43e7eff4cecc857848730f65 730966f44d4e7eda143f3cb4dcb8e4a01581aae8 06fcf0b04cd1891fdecce7047a12c3ea682b9e69 01a91efd651870f78f62b7398e9f1a9e583d250e 300bf2d0ec6d65aa7fa3931c45bc62d620849c6f 1e746740b91196dfcab46d887367182c7efd530f 7733de513ab7b555c32806a7075927754b6a3829 5a466d347c50ce99aec6af864acc5a7323a91b67 a3938a710bfdd9e4740d4b4b2ba6fc5207f91dda 54ca2c8065e1a36b49e89a3eeb7230c0da763019 355692eb86b06a0a23af45c106cfb02c95bf380e eea4ca46542125e02cd7b6de60f28c3710b3f7a3 60512aaa9eae83f8ffd6343582b29470334f053f 60441621f6019b6c7750d75298fefba1f7b4c777 69215c4661cfa673d6dd48e76b4f0b07037387b7 52968c4370dbb3565360d03f52bc7d4cdd80c73d 5560712f77c88e3316b1cfb19279205cef35988c 2edf3a49bdbbb630666c51be9b856d613c9782b3 69e460953d9b13aded4d906b974c0d80c1ceaef7 9d7dcc4187829ada1cc923e0e50b4ee9d428bbd6 7c9bcaa459e18990afd77f1140885ef275494ce5 8e1aeb6cd0a27c2a4b187b53f44aec707afc8df3 b2c64304d14e1bcebc69015a3d820ab941e6d3e7 f5d6d16349324326ff6fb536086fc6c5a29f6c19 8f45d7624129a7d5fcd0e18354ee4a396c74d6c2 086015aa2c44bd2ebd95ab6a1a562e57177c7fa8 b674c44c190bb3813715773a3fb06378ef8ff284 286a0531861cb913f8ac48cd0a04ea0ebd537424 a96cd1ec3f2a976487483c0fa708f8b860664b68 0b0e2aa231f673c33969bfb1d664c1e99fb95c43 7b8e05c8615b4e6369619e5a05d9a8f471f91427 4f6b2ad9f8edb70952c9b6ad1dfab30f7e285f48 6d670fdced072831f8b1d65d00d6a2ab2d92203c 030dff9a7cba9b85be1fec18a895c86673264680 1d011918890db7c5fac80c152488a4792661f434 638d50100fe392ae705a0e5f7d9b47ca1dad3eea f3783fdce9f983bab68e0725485ffa7eb100820c d1d99369f333de03c5eabb58053d8034c011531d a64dde284f657eb303cb73e8b7638df846ec9d04 3152a72d0280f64b83558651d94682a9f1823718 e2a18003d55145e2a3c58fa5215ca2926f86f0ff ebc8e7df1771fc4749fe45ab473a32e84d2abbeb
["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Medicine"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science","Mathematics"] ["Computer Science"] ["Medicine","Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Medicine"] ["Computer Science"] ["Computer Science"] ["Engineering"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Medicine"] ["Computer Science"] ["Computer Science"] ["Computer Science","Engineering"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Physics"] ["Geography","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Engineering","Mathematics"] ["Computer Science"]
2015 2020 2014 2019 2018 2008 2007 2009 2011 2017 2019 2019 2019 2018 2019 2014 2017 2013 2013 2008 2009 2011 2010 2018 2018 2018 2008 2019 2004 2017 2012 2016 2015 2019 2016 2013 2015 2018 2015 2014 2017 2017 2017 2018 2014 2016 2019 2018 2017 2018 2015 2017 2015 2011 2018 2008 2015 2009 2013 2018 2019 2020 2017 2011 2012 2018 2020 2014 2015 2015 2020 2016 2005 2008 2017 2011 2019 2005
IWCMC IV FSKD (4) COMPSAC (1) ICMLC DepCoS-RELCOMEX KDD '14 ISCAS KES (2) ICASSP AIKE SDM ACALCI CAiSE Workshops MICCAI KDD '14 IEEE Transactions on Pattern Analysis and Machine Intelligence EMNLP 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ECCV ArXiv ICLR 2019 IEEE/CVF International Conference on Computer Vision (ICCV) J. Electronic Imaging ICLR ICLR CVPR Workshops ICPP Workshops NIPS ICDCS CIKM-CNIKM IISA CIKM PAKDD (1) AVSS ASCC ISED
https://api.semanticscholar.org/412ccba130dddf63dea07e6b8f5686fd756acd63 https://api.semanticscholar.org/3500b314ae6b3813dfe3b3aa2626be94968ca2e9 https://api.semanticscholar.org/335e3737e9cb1aba7b61c40a79bf9da73e5802cc https://api.semanticscholar.org/e059d46ce443d0c30d06fd907393e9231dbee568 https://api.semanticscholar.org/720b335207fa6819fc2f618bf127622308fa5e73 https://api.semanticscholar.org/f2444054466798f5616ded937b65d8519ac6b6dd https://api.semanticscholar.org/a8f296b3f2cf1914e29878aa68cbcbf3f926142f https://api.semanticscholar.org/9f2264e25b2cb40b03c1a8ea980f97f1052d0c0b https://api.semanticscholar.org/2ad58ec59fa38b117f8c7bde2367c17d8bab9a5a https://api.semanticscholar.org/4a3caf0138672f2dd147384f6b774646ddd9ec21 https://api.semanticscholar.org/22f898b32015c0d5617f78a134a2157224587bf2 https://api.semanticscholar.org/576b1b922676741cb408a5376fce68a0dfe58126 https://api.semanticscholar.org/530f897560f8a84b50c8eb85b1c1eb607bd027bc https://api.semanticscholar.org/910dfa88e49fce4ac0b00ef03607bb3acd4d50a0 https://api.semanticscholar.org/8af595b0e9ec3234f712f5076ce29857ee067568 https://www.semanticscholar.org/paper/fff114cbba4f3ba900f33da574283e3de7f26c83 https://api.semanticscholar.org/327167ab51e001092bbfc2bbac219bb2c2a1ea39 https://api.semanticscholar.org/a281f3025a9b25f6c1753e8f1c06bd327907c0dd https://api.semanticscholar.org/fa757394cc897e60e532553f590f1c15b6acf5f8 https://api.semanticscholar.org/dad16552bb146847d8ff74b9332e7673b230dc48 https://api.semanticscholar.org/a9f11116170626ee5dfbf62ddc936c174fad1e4c https://api.semanticscholar.org/5ad1fa6e0c9c8534a583843bb5d6419251a7addc https://api.semanticscholar.org/f446d9b43cfd3d7c6ca5a373a2259b01fa9850e8 https://api.semanticscholar.org/878b53c6fb66b343e901200e8a88860a2a93d5f2 https://api.semanticscholar.org/9474d6c12f528c8e43e7eff4cecc857848730f65 https://api.semanticscholar.org/730966f44d4e7eda143f3cb4dcb8e4a01581aae8 https://api.semanticscholar.org/06fcf0b04cd1891fdecce7047a12c3ea682b9e69 https://api.semanticscholar.org/01a91efd651870f78f62b7398e9f1a9e583d250e https://api.semanticscholar.org/300bf2d0ec6d65aa7fa3931c45bc62d620849c6f https://api.semanticscholar.org/1e746740b91196dfcab46d887367182c7efd530f https://api.semanticscholar.org/7733de513ab7b555c32806a7075927754b6a3829 https://api.semanticscholar.org/5a466d347c50ce99aec6af864acc5a7323a91b67 https://api.semanticscholar.org/a3938a710bfdd9e4740d4b4b2ba6fc5207f91dda https://api.semanticscholar.org/54ca2c8065e1a36b49e89a3eeb7230c0da763019 https://api.semanticscholar.org/355692eb86b06a0a23af45c106cfb02c95bf380e https://api.semanticscholar.org/eea4ca46542125e02cd7b6de60f28c3710b3f7a3 https://api.semanticscholar.org/60512aaa9eae83f8ffd6343582b29470334f053f https://api.semanticscholar.org/60441621f6019b6c7750d75298fefba1f7b4c777 https://www.semanticscholar.org/paper/6364fdaa0a0eccd823a779fcdd489173f938e91a https://www.semanticscholar.org/paper/fff114cbba4f3ba900f33da574283e3de7f26c83 https://www.semanticscholar.org/paper/b0c065cd43aa7280e766b5dcbcc7e26abce59330 https://www.semanticscholar.org/paper/ee7b883e35d754ae4f71c21bb71f9f03e4ffbb2c https://www.semanticscholar.org/paper/3d5bab25ff8e2c47a07851977635ae8c290d6e14 https://www.semanticscholar.org/paper/3d5d9d8e74b215609eabba80ef79a35ebf460e49 https://www.semanticscholar.org/paper/097851f362667a599208f4a06fa289dac2e91fbe https://www.semanticscholar.org/paper/8388f1be26329fa45e5807e968a641ce170ea078 https://www.semanticscholar.org/paper/0c9ae806059196007938f24d0327a4237ed6adf5 https://www.semanticscholar.org/paper/24730424236724d3f798dec02901e7a1f1c4710e https://www.semanticscholar.org/paper/423fab20c1c39cb6b4d6cf6098f61c092e6d1e3f https://www.semanticscholar.org/paper/15e81c8d1c21f9e928c72721ac46d458f3341454 https://api.semanticscholar.org/69215c4661cfa673d6dd48e76b4f0b07037387b7 https://api.semanticscholar.org/52968c4370dbb3565360d03f52bc7d4cdd80c73d https://api.semanticscholar.org/5560712f77c88e3316b1cfb19279205cef35988c https://api.semanticscholar.org/2edf3a49bdbbb630666c51be9b856d613c9782b3 https://api.semanticscholar.org/69e460953d9b13aded4d906b974c0d80c1ceaef7 https://api.semanticscholar.org/9d7dcc4187829ada1cc923e0e50b4ee9d428bbd6 https://api.semanticscholar.org/7c9bcaa459e18990afd77f1140885ef275494ce5 https://api.semanticscholar.org/8e1aeb6cd0a27c2a4b187b53f44aec707afc8df3 https://api.semanticscholar.org/b2c64304d14e1bcebc69015a3d820ab941e6d3e7 https://api.semanticscholar.org/f5d6d16349324326ff6fb536086fc6c5a29f6c19 https://api.semanticscholar.org/8f45d7624129a7d5fcd0e18354ee4a396c74d6c2 https://api.semanticscholar.org/086015aa2c44bd2ebd95ab6a1a562e57177c7fa8 https://api.semanticscholar.org/b674c44c190bb3813715773a3fb06378ef8ff284 https://api.semanticscholar.org/286a0531861cb913f8ac48cd0a04ea0ebd537424 https://api.semanticscholar.org/a96cd1ec3f2a976487483c0fa708f8b860664b68 https://api.semanticscholar.org/0b0e2aa231f673c33969bfb1d664c1e99fb95c43 https://api.semanticscholar.org/7b8e05c8615b4e6369619e5a05d9a8f471f91427 https://api.semanticscholar.org/4f6b2ad9f8edb70952c9b6ad1dfab30f7e285f48 https://api.semanticscholar.org/6d670fdced072831f8b1d65d00d6a2ab2d92203c https://api.semanticscholar.org/030dff9a7cba9b85be1fec18a895c86673264680 https://api.semanticscholar.org/1d011918890db7c5fac80c152488a4792661f434 https://api.semanticscholar.org/638d50100fe392ae705a0e5f7d9b47ca1dad3eea https://api.semanticscholar.org/f3783fdce9f983bab68e0725485ffa7eb100820c https://api.semanticscholar.org/d1d99369f333de03c5eabb58053d8034c011531d https://api.semanticscholar.org/a64dde284f657eb303cb73e8b7638df846ec9d04 https://api.semanticscholar.org/3152a72d0280f64b83558651d94682a9f1823718 https://api.semanticscholar.org/e2a18003d55145e2a3c58fa5215ca2926f86f0ff https://api.semanticscholar.org/ebc8e7df1771fc4749fe45ab473a32e84d2abbeb