**1. Introduction**

In recent years, the popularity of the Android operation system has attracted the attention of malware developers, whose work has grown rapidly [1,2]. Many malware developers focus on hacking mobile devices and changing them into bots. This allows hackers to access the infected device and other connected devices and form botnets. Botnets are used to execute different malicious attacks, such as distributed denial-of-service (DDoS) attacks, sending spam, data theft, etc. The malicious botnet attacks are developed with advanced techniques (e.g., multi-staged payload or self-protection), making it difficult to identify the malware. This, in turn, poses major threats that require the design of effective approaches to detect these attacks [3].

Android botnets are used to perform attacks on the targeted devices. DDos attacks are achieved by flooding the target machine with superfluous requests and blocking legitimate requests, thus, causing a failure of the targeted system and disruption of the services [4]. Consequently, to protect against such attacks, machine learning methods are

**Citation:** Alkahtani, H.; Aldhyani, T.H.H. Artificial Intelligence Algorithms for Malware Detection in Android-Operated Mobile Devices. *Sensors* **2022**, *22*, 2268. https:// doi.org/10.3390/s22062268

Academic Editors: Leandros Maglaras, Helge Janicke and Mohamed Amine Ferrag

Received: 12 February 2022 Accepted: 11 March 2022 Published: 15 March 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

proven to be effective in detecting and tracking these threats in the internet of things [5,6]. Haystack [7] reported that a third-part of software-development companies manage 70% of the mobile application and control the personal data of users. According to the AV-TEST Security Institute [8], malicious programming increased, with 5.7 million malware Android packages detected by Kaspersky in 2020, three times more than in 2019 (2.1 million). Figure 1 summarize the increase of malware installation packages for smartphone devices in the last five years. Therefore, signature-based malicious installation packages for the extraction of malware patterns relying on their characteristics can be an effective strategy to secure mobile applications.

**Figure 1.** Malware installation packages for smartphone devices.

Malicious attacks occur in different enrolments with a variety of methods such as fuzzing, denial of service, DDoS, port scanning, and probing [9]. These attacks can be threatening to transport, application layers, or different protocols such as internet control message protocol, file transfer protocol, user datagram protocol, simple mail transfer protocol, transmission control protocol, hypertext transfer protocol, etc. Network-based intrusion detection systems can be used to deal with such attacks by scanning the network and detecting them [10].

Usually, in the Android system, security is in-built, where the sandboxing method and permission system are designed to reduce the risk of Android applications [11]. The former was developed using the Linux environment for running Android applications, which allows users to enable permission for the installation of any Android application [12]. However, when updating or upgrading mobile applications, security and privacy features such as time permission, background location, storage, etc., are changed, giving a timeframe for malware attacks. It is possible to exploit Android vulnerabilities during the application developed by users since the Google Play Store cannot detect malicious attacks after the publication of the applications [13]. The percentage of Android malware is presented in Figure 2.

Intrusion detection systems are developed using machine learning and deep learning methods. However, the machine learning technique cannot cope with the huge traffic of data flooding the system. Similarly, deep learning methods fail to provide low generalization errors due to the absence of optimization. Fixed Android botnet datasets make it feasible to design detectors with high detection rates [15], but having complex traffic data hinders the obtention of an accurate prediction rate. This has motivated the development of techniques that are based on Android-malware neuro-evolution classification, thus, providing the number of layers and neurons along with the detection process [16].

The present study aimed to extract static and dynamic features from unknown applications; these features show if a particular application is "normal" or "attack". These features are used to examine the performance of several machine learning and deep learning models, including the k-nearest neighbors (KNN) [17], support vector machine (SVM) [18], convolutional neural networks (CNN) [19], dense neural networks [20], gated recurrent units (GRU), long short-term memory (LSTM) [21], and the hybrid deep learning convolutional neural networks long/short-term memory (CNN-LSTM) and convolutional neural networks/gated recurrent units CNN-GRU [22] methods.

In this study, we investigated and estimated the performance of various machine learning and deep learning algorithms in the detection of mobile malware attacks. This study offers the optimal algorithms for the monitoring of Android applications against malicious attacks. Thus, our research aims to contribute to this field with the following:

### **2. Background of Study**

This section offers an overview of previous research related to intrusion detection systems, Android malware detection, and standard datasets of Android malicious attacks. Furthermore, it provides an overview of the machine learning and deep learning approaches applied to the design of cybersecurity systems.

The regular improvement of sophisticated Android malware families, e.g., Chamois malware, has made the task of detecting malicious attacks daunting. To tackle this, researchers developed machine learning techniques that improved the available systems. Recently, many studies have applied machine learning models for Android botnet detection, such as linear regression, KNN [23], SVM, and decision trees (DT) algorithms [24]. Some of these recent studies [25,26] used deep learning algorithms, although they do not provide a thorough understanding of their effectiveness. Therefore, the current study compares with deep learning models to examine their effectiveness in Android botnet detection with the use of the available installation support center of expertise (ISCX) botnet dataset [27–29].

Kadir et al. [30] used deep learning models to analyze Android botnet attacks in an attempt to understand the latter's hidden features. The system was evaluated using the ISCX Android botnet dataset, which contained 1929 samples. Anwar et al. [31] proposed an Android botnet detection approach based on static functions. The features of permissions, MD5 signatures, and broadcast receivers were combined and processed with machine learning algorithms. The input data collected from the ISCX dataset were 1400 from different botnet applications, with the system achieving an accuracy of 95.1% in distinguishing Android botnet attacks [32].

Several machine learning algorithms were proposed to classify normal and abnormal botnet attacks. In one study, the results indicated that the random forest approach had 0.972% precision and 0.96% recall. In [33], machine learning approaches were proposed for detecting Android botnets. The ISCX dataset consisted of 1635 benign and 1635 attacks. The random forest tree model achieved 97%. In another study [34], the DT, Naive Bayes, and random forest machine learning algorithms were used to detect Android attacks. The information gain method was used to select the significant features. The random forest algorithm achieved a 94.6% accuracy. Karim et al. [35] proposed the static analysis approach to explore the pattern of the features of Android botnet attacks. The features were compared with the intrusion application using the Drebin dataset [36]. Artificial intelligence (AI) approaches using a knowledge-based system were used to secure Android mobiles against malicious attacks [37,38]. Inspired by a meta-heuristic rule and based on fuzzy logic, intrusion detection and data mining systems were developed [39], while machine learning approaches were applied in the development of IDS applications [40–42]. The design of IDS systems employed the artificial bee colony [43], particle swarm optimization [44], grey wolf optimization [45], and artificial fish swarm [46] algorithms.

Many systems were developed based on signature-based Android malware detection approaches and behavior-based Android malware intrusion detection approaches [47]. The former is a simple detection method that manages intrusions' low degree of false positives. The latter is based on anomaly detection and is a very common method using AI algorithms to detect malicious attacks. Numerous research articles aimed to detect and classify Android malware and attacks using machine learning and deep learning approaches, such as the DT and deep learning approaches [48]. By using the generative adversarial networks algorithm [49], it was shown that traditional machine learning was successful in detecting malware in an Android environment [50].

Most of the published studies used datasets from Google Play [51], AndroZoo, Android Permission [52], Andrototal [53], Wandoujia [54], Kaggle [55], and CICMaldroid [56]. The present study aimed at developing a system to detect malware attacks in Android environments that have an in-built security system. However, there are still many Android applications with design weaknesses and security flaws that can be threatening to end-users. Therefore, it is crucial to use machine learning and deep learning algorithms to detect Android malware and vulnerability analysis to prevent the development of malware and attacks by hackers [57,58].

### **3. Materials and Methods**

In 2008, Android was developed. With the increasing number of Android applications, companies immediately discussed and built security tools [2]. Nevertheless, the Android system is suffering from security weaknesses. In the last five years, AI approaches focused on protecting the Android system, with many researchers studying the appropriate AI approaches to obtain high accuracy. The framework of the present research is presented in Figure 3. The machine learning algorithms support vector machine (SVM), k-nearest neighbors (KNN), linear discriminant analysis (LDA) and the deep learning algorithms long short-term memory (LSTM), convolution neural network-long short-term memory (CNN-LSTM), and autoencoder algorithms were used to detect malware and attacks against Android applications. These algorithms were tested using two standard datasets. The research questions of this study were:

**Figure 3.** A generic representation of the models applied for the detection of Android malware.


*Review* **Phenolic Compounds' Occurrence in** *Opuntia* **Species and Their Role in the Inflammatory Process: A Review**

**Walid Zeghbib 1,2, Fares Boudjouan 3,4, Vitor Vasconcelos 2,5 and Graciliana Lopes 2,\***

**Abstract:** Within the *Cactaceae* family, *Opuntia* comprises the most widespread species, with a recognized importance in human life, including feeding, domestic use as home natural barriers, and as a traditional remedy for diverse diseases and conditions such asthma, edema, and burns. Indeed, scientific reports have stated that these health benefits may be due to various active compounds, particularly polyphenols, which are ubiquitously found in plants and have proven their pharmacological efficiency by displaying antimicrobial, anti-cancer, and anti-inflammatory activities, among others. *Opuntia* species contain different classes of phenolic compounds that are recognized for their anti-inflammatory potential. Among them, quercetin, isorhamnetin, and kaempferol derivates were reported to greatly contribute to modulate cells' infiltration and secretion of soluble inflammatory mediators, with key implications in the inflammatory process. In this review, we make a summary of the different classes of phenolic compounds reported in *Opuntia* species so far and explore their implications in the inflammatory process, reported by in vitro and in vivo bioassays, supporting the use of cactus in folk medicine and valorizing them from the socio-economic point of view.

**Keywords:** *Opuntia* sp.; prickly pear; phenolic compounds; flavonoids; inflammation


**TEXTILES FOR ADVANCED APPLICATIONS**

Edited by **Bipin Kumar** and **Suman Thakur**

#### **Textiles for Advanced Applications**

http://dx.doi.org/10.5772/66015 Edited by Bipin Kumar and Suman Thakur

#### **Contributors**

Murat Eyvaz, Serkan Arslan, Ercan Gürbulak, Ebubekir Yüksel, Izabela Luiza Ciesielska-Wrobel, Ivana Schwarz, Stana Kovacevic, Jacqueline Domjanić, Snježana Brnada, Urška Rozman, Sonja Šostar Turk, Emil Pal, Vida Gönc, PhD Daniela Zavec Pavlinić, Peng Wang, Xavier Legrand, Damien Soulat, Felecia Davis, Nesrin Karaduman, Yekta Karaduman, Hüseyin Özdemir, Gökce Özdemir, Adrian Buhu, Liliana Buhu, Alenka Pavko Čuden, Darja Rant, Nazan Kalebek, Gabriela Telipan, Beatrice Moasa, Elena Helerea, Razvan Scarlat, Eftalea Carpus, Gheorghe Enache, Mutlu Kurban, Osman Babaarslan, İsmail Hakkı Çağatay, Suman Thakur, Adriana Ungureanu

#### **© The Editor(s) and the Author(s) 2017**

The moral rights of the and the author(s) have been asserted.

All rights to the book as a whole are reserved by INTECH. The book as a whole (compilation) cannot be reproduced, distributed or used for commercial or non-commercial purposes without INTECH's written permission. Enquiries concerning the use of the book should be directed to INTECH rights and permissions department (permissions@intechopen.com).

Violations are liable to prosecution under the governing Copyright Law.

Individual chapters of this publication are distributed under the terms of the Creative Commons Attribution 3.0 Unported License which permits commercial use, distribution and reproduction of the individual chapters, provided the original author(s) and source publication are appropriately acknowledged. If so indicated, certain images may not be included under the Creative Commons license. In such cases users will need to obtain permission from the license holder to reproduce the material. More details and guidelines concerning content reuse and adaptation can be foundat http://www.intechopen.com/copyright-policy.html.

#### **Notice**

Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book.

First published in Croatia, 2017 by INTECH d.o.o. eBook (PDF) Published by IN TECH d.o.o. Place and year of publication of eBook (PDF): Rijeka, 2019. IntechOpen is the global imprint of IN TECH d.o.o. Printed in Croatia

Legal deposit, Croatia: National and University Library in Zagreb

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Textiles for Advanced Applications Edited by Bipin Kumar and Suman Thakur p. cm. Print ISBN 978-953-51-3500-5 Online ISBN 978-953-51-3501-2 eBook (PDF) ISBN 978-953-51-4648-3


**2. Germination in the soil and standard establishment**

Germination is the fundamental process by which different plant species grow from a single seed into a young plant. This process essentially influences both crops yield and quality, here the seed observes water by the passage of time, chilling, warming, oxygen availability, and light exposure may all operate while initiating the process, the rehydration will expand the cell embryo which increases the rate of respiration, and various metabolic processes suspended or much reduced during dormancy resume. These events in the life of seeds are associated with anatomical changes in cell organelles (membranous bodies concerned with metabolism), in the cell of the embryo inside the stigma, whereas each seed reacts individually to its microenvironment. A field consists of a wide range of microenvironments, how seeds establish & germinate under field conditions hence the uniformity of crops associated with seedbed preparation (perry 1973; Hadas wolf), the performance of crops' physical and chemical properties is as per the stability climatic uncertainty and traffic history. Hence germination is the preliminary stage to represent the quality of seeds (**Figure 1**) [2].

*Seed Biology and Phytochemistry for Sustainable Future DOI: http://dx.doi.org/10.5772/intechopen.106208*

#### **Figure 1.**

*Diagrammatic representation of part of the molecular signaling and components acting on the termination of coat-imposed dormancy and the induction of germination. Broken lines and question marks represent probable but unconfirmed interactions.*


*Supporting open minds since 2005*

Oral and Maxillofacial Surgery http://dx.doi.org/10.5772/intechopen.87318 Edited by Gokul Sridharan

#### Contributors

Omar Sheikh, Matin Madadian, Amanveer Benning, Aynur Emine Cicekcibasi, Gülay Acar, Krishna Sireesha Sundaragiri, Soumya Makarla, Bharat Sankhla, Tina Nazerani, Reingard M. Aigner, Peter Kalmar, Melike Aytekin, Volkan Arisan, Leandro Díez Suárez, Antonio Gómez Pedraza, Vicente González Cardín, Martín Granados García, Kasi Ganesh Sriraam, K. Rajendran Arun Vignesh, Onur Gonul, Ahmet Usame Çiçek, Onur Atalı, Faysal Uğurlu, Murat Afat

#### © The Editor(s) and the Author(s) 2021

The rights of the editor(s) and the author(s) have been asserted in accordance with the Copyright, Designs and Patents Act 1988. All rights to the book as a whole are reserved by INTECHOPEN LIMITED. The book as a whole (compilation) cannot be reproduced, distributed or used for commercial or non-commercial purposes without INTECHOPEN LIMITED's written permission. Enquiries concerning the use of the book should be directed to INTECHOPEN LIMITED rights and permissions department (permissions@intechopen.com).

Violations are liable to prosecution under the governing Copyright Law.

Individual chapters of this publication are distributed under the terms of the Creative Commons Attribution 3.0 Unported License which permits commercial use, distribution and reproduction of the individual chapters, provided the original author(s) and source publication are appropriately acknowledged. If so indicated, certain images may not be included under the Creative Commons license. In such cases users will need to obtain permission from the license holder to reproduce the material. More details and guidelines concerning content reuse and adaptation can be found at http://www.intechopen.com/copyright-policy.html.

#### Notice

Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book.

First published in London, United Kingdom, 2021 by IntechOpen IntechOpen is the global imprint of INTECHOPEN LIMITED, registered in England and Wales, registration number: 11086078, 5 Princes Gate Court, London, SW7 2QJ, United Kingdom Printed in Croatia

British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library

Additional hard and PDF copies can be obtained from<EMAIL_ADDRESS>
Oral and Maxillofacial Surgery Edited by Gokul Sridharan p. cm. Print ISBN 978-1-83880-335-3 Online ISBN 978-1-83880-336-0 eBook (PDF) ISBN 978-1-83880-460-2


*3.5.6 EM-fermented fish (EM-F.F)*

To allow smooth plant growth over time and from one season to the next, EM-F.F is added to the soil, thus releasing nutrients such as phosphorus and nitrogen slowly over time. The fish are ground to fine powder during preparation to enable accessibility to microorganisms. Before spraying on the plants, the resultant mixture is combined with multiplied EM [3, 35, 36].