Thus , security researchers and the anti-virus industry are facing a herculean task in extracting payloads hidden within packed executables . It is a common practice to use manual unpacking or static unpacking using some software tools and analyse the application programming interface ( API ) calls for malware detection . However , extracting these features from the unpacked executables for reverse obfuscation is labour intensive and requires deep knowledge of low-level programming that includes kernel and assembly language . A comparison between the methods provided in this research and the results of other studies that used the same dataset is given . The results show that Extra-trees classifier had achieved the highest weighted accuracy percentage among the other classifiers by 87.75 % for malware detection and 79.97 % for malware categorization . Finally , this study has achieved significant enhancement in malware categorization rate by 30.2 % for precision and 31.14 % recall in comparison with other studies that used the same dataset . In this paper , to fight against malware variants and zero-day malware , we proposed DroidChain : a method combining static analysis and a behavior chain model . We transform the malware detection problem into more accessible matrix form . Using this method , we propose four kinds of malware models , including privacy leakage , SMS financial charges , malware installation , and privilege escalation . We propose an high order theory for ANI , later called HOANI , that allows to study program dependencies . Our idea is then to formalize and study the malware detection problem in terms of HOANI . The most common cyber threats targeting end users and terminals are caused by malicious software , called malware . The malware detection process can be performed either by matching their digital signatures or analyzing their behavioral models . As the obfuscation techniques make the malware almost undetectable , the classic signature-based anti-virus tools must be supported with behavioral analysis . This paper has a survey and analysis of various research works on Malware Detection using behavior characteristics and also introduces its problems and issues . Finally , we have compared various machine learning algorithms which can be used for most effective malware detection process . The implementation and the results of the study show that the Random Forest algorithm is a most efficient algorithm for detection of malicious files in any system . Cryptography reversing of software has playing an important role in malware detecting and vulnerability mining recently . This paper has proposed a four-level cryptography reversing model which contains crypto algorithms recognition , crypto mechanism reversing , wrong/weak implement , vulnerability mining . Malware variants are one of the major challenges in malware detecting today . Obfuscation , as a most popular technology to generate these variants , can change the signatures of malware to avoid the current signature-based malware preventing method , which is a big threat to information system . The log file may be effectively utilized in analyzing app ’s behavior and intentions , which will prove to be useful in several application domains . For example , this tool may be used in developing malware detection analysis based upon the API call , identifying resource usages in different app components , developing the tool for analyzing the application ’s behavior , and constructing efficient mobile anti-malware tools , etc . We proposed a twostep model which combines static and dynamic analysis approaches . In this paper , we studied the way that classify whether unknown PE file is malware or not . In the classification problem of malware detection domain , feature extraction and classifier are important . For that purpose , we studied what the feature is good for classifier and the which classifier is good ... The analysis of applied scientific researches devoted to creation of malware protection systems suggests that the improvement of mathematical tools using modern neural network models based on deep neural networks is a promising trend in the development of malware detection systems . Also , the results of analysis have determined the need to create a development method for the deep neural network architecture suitable for use within the modern malware detection means . As part of the study , a method for developing a deep neural network architecture designed to detect malicious software has been suggested . Effective and efficient malware detection is at the forefront of research into building secure digital systems . As with many other fields , malware detection research has seen a dramatic increase in the application of machine learning algorithms . One machine learning technique that has been used widely in the field of pattern matching in general — and malware detection in particular — is hidden Markov models ( HMMs ) . We present Privacy-Enhanced Filtering ( PEF ) , a model-driven prototype framework that relies on declarative descriptions of protocols and a set of filter rules , which are used to automatically transform network traffic data to remove sensitive information . This paper discusses the design , implementation and application of PEF , which is available as open-source software and configured for use in a typical malware detection scenario . However , most of research are still focused on binary classification issue , which predicts a file as benign or malicious . Only a small fraction of them work on malware type detection or classification of malware family . This work mainly uses several machine learning models to build static malware type classifiers on PE-format files . This paper explores the area of adversarial examples for malware detection . By training an existing model on a production-scale dataset , we show that some previous attacks are less effective than initially reported , while simultaneously highlighting architectural weaknesses that facilitate new attack strategies for malware classification . Finally , we explore how generalizable different attack strategies are , the trade-offs when aiming to increase their effectiveness , and the transferability of single-step attacks . In this paper , we develop an automatic categorization system to automatically group phishing websites or malware samples using a cluster ensemble by aggregating the clustering solutions that are generated by different base clustering algorithms . We propose a principled cluster ensemble framework to combine individual clustering solutions that are based on the consensus partition , which can not only be applied for malware categorization , but also for phishing website clustering . In addition , the domain knowledge in the form of sample-level/website-level constraints can be naturally incorporated into the ensemble framework . Semi-supervised learning where both labeled and unlabeled data are used to learn a good model quickly is a natural choice under such condition . We investigate semi-supervised classification for malware categorization . We observed that malware data have specific characteristics and that they are noisy . To deal with learning from few samples , term “ one-shot learning ” or “ fewshot learning ” was introduced , and mostly used in computer vision to recognize images , handwriting , etc . An approach introduced in this paper takes advantage of One-shot learning algorithms in solving the malware classification problem by using Memory Augmented Neural Network in combination with malware ’s API calls sequence , which is a very valuable source of information for identifying malware behavior . In addition , it also use some advantages of the development in Natural Language Processing field such as word2vec , etc . to convert those API sequences to numeric vectors before feeding to the one-shot learning network . One of the fields where this technique is currently being applied is in the malware classification problem . The malware classification problem has an increasing complexity , due to the growing number of features needed to represent the behaviour of the application as exhaustively as possible . Although other classification methods , as those based on SVM , have been traditionally used , the DNN pose a promising tool in this field . Several research studies such as that of converting malware into gray-scale images have helped to improve the task of classification in the sense that it is easier to use an image as input to a model that uses Deep Learning ’s Convolutional Neural Network . In this paper , we propose a Con-volutional Neural Network model for malware image classification that is able to reach 98 % accuracy . In this work , we applied a deep Convolutional Neural Network ( CNN ) with Xception model to perform malware image classification . The Xception model is a recently developed special CNN architecture that is more powerful with less over- fitting problems than the current popular CNN models such as VGG16 . Towards Malware Classification Interpretation . Machine learning ( ML ) based approach is considered as one of the most promising techniques for Android malware detection and has achieved high accuracy by leveraging commonly-used features . This article presents SignatureMiner , a semisupervised security framework for Anti-Virus signatures featuring normalization , customization , clustering and knowledge discovery . SignatureMiner is based on MinHash and regular expressions and can be used both for malware label classification and signature-based analytics . This paper introduces a novel computational implementation of a Cognitive Modeling Architecture ( CMA ) which incorporates concepts of consciousness . We apply our model to the malware type-classification task . The underlying methodology and theories are generalizable to many domains . Based on the API information , we innovatively construct Boolean , frequency , and time-series data sets . Based on these three data sets , three detection models for Android malware detection regarding API calls , API frequency , and API sequence aspects are constructed . Ultimately , an ensemble model is constructed for conformity . Abstract The academic security research community has studied the Android malware detection problem extensively . Machine learning methods proposed in previous work typically achieve high reported detection performance on fixed datasets . Permission-based access control model is one of the most important mechanisms to protect Android apps against malware . In this paper , we propose a new permission-based model that enhances the efficiency and accuracy of Android malware analysis and detection , and has the capability of potentially detecting previously unknown malware . In this new model , we improve the feature selection by introducing a new weighting method , named TF-IDFCF , based on the class frequency ( CF ) of the feature . Malware detection for Android OS has become an upcoming research problem of interest . Dynamic Taint Analysis is an efficient analysis in existing Android malware detection analysis . The aim of this paper is to work towards Dynamic Taint Analysis of Android malware using automatic tagging and with out modification in Android platform . Additionally , our experiments show that XMal can also interpret the reason why some samples are misclassified by ML classifiers . Our study peeks into the interpretable ML through the research of Android malware detection and analysis . Experimental validation show high performance with 95 % precision for identifying malware , demonstrating that PCLs can be used for discriminating malicious apps from benign apps . By further investigating the generalization ability of this feature set , we highlight an issue often overlooked in the Android malware detection community : Qualitative aspects of training datasets have a strong impact on a malware detector ’s performance . This work proposes a hybrid feature selection technique based on Rough Set Quick Reduct algorithm with Community Detection scheme . The proposed technique is applied in Android malware detection domain and compared with the performances of existing feature selectors . It produces highest average classification accuracy of 97.88 % and average ROC values up to 0.987 . When training the classifer , the algorithm optimizes and complements the training set to make the training data more reasonable . Experiments show that the Android malware detection system based on improved SVM algorithm is superior to the traditional SVM algorithm in the classification accuracy and detection rate of false positives , and with the increase of the training sample set greatly reduces the training time , this Android malware detection technology is more suitable for Android malware detection environment . We then show that our framework can aid analysts who attempt to study unknown malware families . Finally , we discuss what our findings mean for Android malware detection research , highlighting areas that need further attention by the research community . Currently available systems do not meet the analysts ' needs which are described as : visual access suitable for complex data structures , visual representations appropriate for IT-security experts , provision of workflow-specific interaction techniques , and the ability to externalize knowledge in the form of rules to ease the analysis process and to share with colleagues . To close this gap , we designed and developed KAMAS , a knowledge-assisted visualization system for behavior-based malware analysis . This paper is a design study that describes the design , implementation , and evaluation of the prototype . To mitigate security problem brought by Android malware , various work has been proposed such as behavior based malware detection and data mining based malware detection . In this paper , we put forward a novel Android malware detection model using data mining techniques . We study the usefulness of quantitative data flow analyses for behavior-based malware detection . To this end , we propose a generic model to represent system behavior , i.e. traces of system calls , as sequences of quantifiable data flows that together form a Quantitative Data Flow Graph ( QDFG ) . Proximity malware is a class of malware that exploits the opportunistic contacts and distributed nature of DTNs for propagation . Behavioral characterization of malware is an effective alternative to pattern matching in detecting malware , especially when dealing with polymorphic or obfuscated malware . In this paper , we first propose a general behavioral characterization of proximity malware which based on naive Bayesian model , which has been successfully applied in non-DTN settings such as filtering email spams and detecting botnets . Proximity malware is a malicious program that spreads critically . Behavioral characterization of malware is an effective alternative to pattern matching in detecting malware , especially when dealing with polymorphic or obfuscated malware . In this paper we first propose a frequency based method to detect the malware in delay tolerant network . ( Behavior based malware classification using online machine learning ) . Recently , malware , short for malicious software has greatly evolved and became a major threat to the home users , enterprises , and even to the governments .
Thus , security researchers and the anti-virus industry are facing a herculean task in extracting payloads hidden within packed executables . It is a common practice to use manual unpacking or static unpacking using some software tools and analyse the application programming interface ( API ) calls for malware detection . However , extracting these features from the unpacked executables for reverse obfuscation is labour intensive and requires deep knowledge of low-level programming that includes kernel and assembly language .
A comparison between the methods provided in this research and the results of other studies that used the same dataset is given . The results show that Extra-trees classifier had achieved the highest weighted accuracy percentage among the other classifiers by 87.75 % for malware detection and 79.97 % for malware categorization . Finally , this study has achieved significant enhancement in malware categorization rate by 30.2 % for precision and 31.14 % recall in comparison with other studies that used the same dataset .
In this paper , to fight against malware variants and zero-day malware , we proposed DroidChain : a method combining static analysis and a behavior chain model . We transform the malware detection problem into more accessible matrix form . Using this method , we propose four kinds of malware models , including privacy leakage , SMS financial charges , malware installation , and privilege escalation .
We propose an high order theory for ANI , later called HOANI , that allows to study program dependencies . Our idea is then to formalize and study the malware detection problem in terms of HOANI .
The most common cyber threats targeting end users and terminals are caused by malicious software , called malware . The malware detection process can be performed either by matching their digital signatures or analyzing their behavioral models . As the obfuscation techniques make the malware almost undetectable , the classic signature-based anti-virus tools must be supported with behavioral analysis .
This paper has a survey and analysis of various research works on Malware Detection using behavior characteristics and also introduces its problems and issues . Finally , we have compared various machine learning algorithms which can be used for most effective malware detection process . The implementation and the results of the study show that the Random Forest algorithm is a most efficient algorithm for detection of malicious files in any system .
Cryptography reversing of software has playing an important role in malware detecting and vulnerability mining recently . This paper has proposed a four-level cryptography reversing model which contains crypto algorithms recognition , crypto mechanism reversing , wrong/weak implement , vulnerability mining .
Malware variants are one of the major challenges in malware detecting today . Obfuscation , as a most popular technology to generate these variants , can change the signatures of malware to avoid the current signature-based malware preventing method , which is a big threat to information system .
The log file may be effectively utilized in analyzing app ’s behavior and intentions , which will prove to be useful in several application domains . For example , this tool may be used in developing malware detection analysis based upon the API call , identifying resource usages in different app components , developing the tool for analyzing the application ’s behavior , and constructing efficient mobile anti-malware tools , etc . We proposed a twostep model which combines static and dynamic analysis approaches .
In this paper , we studied the way that classify whether unknown PE file is malware or not . In the classification problem of malware detection domain , feature extraction and classifier are important . For that purpose , we studied what the feature is good for classifier and the which classifier is good ...
The analysis of applied scientific researches devoted to creation of malware protection systems suggests that the improvement of mathematical tools using modern neural network models based on deep neural networks is a promising trend in the development of malware detection systems . Also , the results of analysis have determined the need to create a development method for the deep neural network architecture suitable for use within the modern malware detection means . As part of the study , a method for developing a deep neural network architecture designed to detect malicious software has been suggested .
Effective and efficient malware detection is at the forefront of research into building secure digital systems . As with many other fields , malware detection research has seen a dramatic increase in the application of machine learning algorithms . One machine learning technique that has been used widely in the field of pattern matching in general — and malware detection in particular — is hidden Markov models ( HMMs ) .
We present Privacy-Enhanced Filtering ( PEF ) , a model-driven prototype framework that relies on declarative descriptions of protocols and a set of filter rules , which are used to automatically transform network traffic data to remove sensitive information . This paper discusses the design , implementation and application of PEF , which is available as open-source software and configured for use in a typical malware detection scenario .
However , most of research are still focused on binary classification issue , which predicts a file as benign or malicious . Only a small fraction of them work on malware type detection or classification of malware family . This work mainly uses several machine learning models to build static malware type classifiers on PE-format files .
This paper explores the area of adversarial examples for malware detection . By training an existing model on a production-scale dataset , we show that some previous attacks are less effective than initially reported , while simultaneously highlighting architectural weaknesses that facilitate new attack strategies for malware classification . Finally , we explore how generalizable different attack strategies are , the trade-offs when aiming to increase their effectiveness , and the transferability of single-step attacks .
In this paper , we develop an automatic categorization system to automatically group phishing websites or malware samples using a cluster ensemble by aggregating the clustering solutions that are generated by different base clustering algorithms . We propose a principled cluster ensemble framework to combine individual clustering solutions that are based on the consensus partition , which can not only be applied for malware categorization , but also for phishing website clustering . In addition , the domain knowledge in the form of sample-level/website-level constraints can be naturally incorporated into the ensemble framework .
Semi-supervised learning where both labeled and unlabeled data are used to learn a good model quickly is a natural choice under such condition . We investigate semi-supervised classification for malware categorization . We observed that malware data have specific characteristics and that they are noisy .
To deal with learning from few samples , term “ one-shot learning ” or “ fewshot learning ” was introduced , and mostly used in computer vision to recognize images , handwriting , etc . An approach introduced in this paper takes advantage of One-shot learning algorithms in solving the malware classification problem by using Memory Augmented Neural Network in combination with malware ’s API calls sequence , which is a very valuable source of information for identifying malware behavior . In addition , it also use some advantages of the development in Natural Language Processing field such as word2vec , etc . to convert those API sequences to numeric vectors before feeding to the one-shot learning network .
One of the fields where this technique is currently being applied is in the malware classification problem . The malware classification problem has an increasing complexity , due to the growing number of features needed to represent the behaviour of the application as exhaustively as possible . Although other classification methods , as those based on SVM , have been traditionally used , the DNN pose a promising tool in this field .
Several research studies such as that of converting malware into gray-scale images have helped to improve the task of classification in the sense that it is easier to use an image as input to a model that uses Deep Learning ’s Convolutional Neural Network . In this paper , we propose a Con-volutional Neural Network model for malware image classification that is able to reach 98 % accuracy .
In this work , we applied a deep Convolutional Neural Network ( CNN ) with Xception model to perform malware image classification . The Xception model is a recently developed special CNN architecture that is more powerful with less over- fitting problems than the current popular CNN models such as VGG16 .
Towards Malware Classification Interpretation . Machine learning ( ML ) based approach is considered as one of the most promising techniques for Android malware detection and has achieved high accuracy by leveraging commonly-used features .
This article presents SignatureMiner , a semisupervised security framework for Anti-Virus signatures featuring normalization , customization , clustering and knowledge discovery . SignatureMiner is based on MinHash and regular expressions and can be used both for malware label classification and signature-based analytics .
This paper introduces a novel computational implementation of a Cognitive Modeling Architecture ( CMA ) which incorporates concepts of consciousness . We apply our model to the malware type-classification task . The underlying methodology and theories are generalizable to many domains .
Based on the API information , we innovatively construct Boolean , frequency , and time-series data sets . Based on these three data sets , three detection models for Android malware detection regarding API calls , API frequency , and API sequence aspects are constructed . Ultimately , an ensemble model is constructed for conformity .
Abstract The academic security research community has studied the Android malware detection problem extensively . Machine learning methods proposed in previous work typically achieve high reported detection performance on fixed datasets .
Permission-based access control model is one of the most important mechanisms to protect Android apps against malware . In this paper , we propose a new permission-based model that enhances the efficiency and accuracy of Android malware analysis and detection , and has the capability of potentially detecting previously unknown malware . In this new model , we improve the feature selection by introducing a new weighting method , named TF-IDFCF , based on the class frequency ( CF ) of the feature .
Malware detection for Android OS has become an upcoming research problem of interest . Dynamic Taint Analysis is an efficient analysis in existing Android malware detection analysis . The aim of this paper is to work towards Dynamic Taint Analysis of Android malware using automatic tagging and with out modification in Android platform .
Additionally , our experiments show that XMal can also interpret the reason why some samples are misclassified by ML classifiers . Our study peeks into the interpretable ML through the research of Android malware detection and analysis .
Experimental validation show high performance with 95 % precision for identifying malware , demonstrating that PCLs can be used for discriminating malicious apps from benign apps . By further investigating the generalization ability of this feature set , we highlight an issue often overlooked in the Android malware detection community : Qualitative aspects of training datasets have a strong impact on a malware detector ’s performance .
This work proposes a hybrid feature selection technique based on Rough Set Quick Reduct algorithm with Community Detection scheme . The proposed technique is applied in Android malware detection domain and compared with the performances of existing feature selectors . It produces highest average classification accuracy of 97.88 % and average ROC values up to 0.987 .
When training the classifer , the algorithm optimizes and complements the training set to make the training data more reasonable . Experiments show that the Android malware detection system based on improved SVM algorithm is superior to the traditional SVM algorithm in the classification accuracy and detection rate of false positives , and with the increase of the training sample set greatly reduces the training time , this Android malware detection technology is more suitable for Android malware detection environment .
We then show that our framework can aid analysts who attempt to study unknown malware families . Finally , we discuss what our findings mean for Android malware detection research , highlighting areas that need further attention by the research community .
Currently available systems do not meet the analysts ' needs which are described as : visual access suitable for complex data structures , visual representations appropriate for IT-security experts , provision of workflow-specific interaction techniques , and the ability to externalize knowledge in the form of rules to ease the analysis process and to share with colleagues . To close this gap , we designed and developed KAMAS , a knowledge-assisted visualization system for behavior-based malware analysis . This paper is a design study that describes the design , implementation , and evaluation of the prototype .
To mitigate security problem brought by Android malware , various work has been proposed such as behavior based malware detection and data mining based malware detection . In this paper , we put forward a novel Android malware detection model using data mining techniques .
We study the usefulness of quantitative data flow analyses for behavior-based malware detection . To this end , we propose a generic model to represent system behavior , i.e. traces of system calls , as sequences of quantifiable data flows that together form a Quantitative Data Flow Graph ( QDFG ) .
Proximity malware is a class of malware that exploits the opportunistic contacts and distributed nature of DTNs for propagation . Behavioral characterization of malware is an effective alternative to pattern matching in detecting malware , especially when dealing with polymorphic or obfuscated malware . In this paper , we first propose a general behavioral characterization of proximity malware which based on naive Bayesian model , which has been successfully applied in non-DTN settings such as filtering email spams and detecting botnets .
Proximity malware is a malicious program that spreads critically . Behavioral characterization of malware is an effective alternative to pattern matching in detecting malware , especially when dealing with polymorphic or obfuscated malware . In this paper we first propose a frequency based method to detect the malware in delay tolerant network .
( Behavior based malware classification using online machine learning ) . Recently , malware , short for malicious software has greatly evolved and became a major threat to the home users , enterprises , and even to the governments .
Towards Understanding Malware Behaviour by the Extraction of API Calls Android Malware Detection and Categorization Based on Conversation-level Network Traffic Features DroidChain: A novel Android malware detection method based on behavior chains Analyzing program dependencies for malware detection Detection and Modeling of Cyber Attacks with Petri Nets A STUDY ON DIGITAL FORENSICS USING VARIOUS ALGORITHMS FOR MALWARE DETECTION Hybrid Method to Analyze Cryptography in Software An Anti-obfuscation Malware Variants Identification System Detecting Malicious Behavior of Android Applications 멀웨어 검출을 위한 기계학습 알고리즘과 특징 추출에대한 성능연구 Malware Detection Using Artificial Neural Networks Hidden Markov models with random restarts versus boosting for malware detection Model-driven software engineering in practice: privacy-enhanced filtering of network traffic Static PE Malware Type Classification Using Machine Learning Techniques Exploring Adversarial Examples in Malware Detection Ensemble Clustering for Internet Security Applications Towards Better Semi-Supervised Classification of Malicious Software One-shot Learning Approach for Unknown Malware Classification Evolving Deep Neural Networks architectures for Android malware classification Malware Images Classification Using Convolutional Neural Network An Xception Convolutional Neural Network for Malware Classification with Transfer Learning Why an Android App is Classified as Malware? Towards Malware Classification Interpretation SignatureMiner: A Fast Anti-Virus Signature Intelligence Tool QuEST for malware type-classification A Combination Method for Android Malware Detection Based on Control Flow Graphs and Machine Learning Algorithms A pragmatic android malware detection procedure Mining and Detection of Android Malware Based on Permissions AndroTaint: An efficient android malware detection framework using dynamic taint analysis Why an Android App is Classified as Malware? Towards Malware Classification Interpretation A Study of Potential Component Leaks in Android Apps A Hybrid Community Based Rough Set Feature Selection Technique in Android Malware Detection Research on Android Malware Detection Technology Based on Improved SVM Algorithm Eight Years of Rider Measurement in the Android Malware Ecosystem: Evolution and Lessons Learned A knowledge-assisted visual malware analysis system: Design, validation, and reflection of KAMAS API Sequences Based Malware Detection for Android Behavior-based Malware Detection with Quantitative Data Flow Analysis Behavioral Malware Detection in Delay Tolerant Networks Behavioral Malware Detection in Dtn Using Intrusion Detection System Classification des logiciels malveillants basée sur le comportement à l'aide de l'apprentissage automatique en ligne. (Behavior based malware classification using online machine learning)
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PPREW@POPL ESEC/SIGSOFT FSE IWSPA@CODASPY CEC NTMS CNS UIC/ATC/ScalCom
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