Despite only being around for a few years, mobile devices have steadily risen to become the most extensively used computer devices. Given the number of people who rely on smartphones, which can install third-party apps, it has become an increasingly important issue for end-users and service providers to ensure that both the devices and the underlying network are secure. People will become more reliant on applications such as SMS, MMS, Internet Access, Online Transactions, and so on due to such features and capabilities. Thousands of devices ranging from low-cost phones to high-end luxury phones are powered by the Android operating system, which has dominated the smartphone marketplace. It is about making it possible for people from all socioeconomic backgrounds to get and use mobile devices in their daily activities. In response to this growing popularity, the number of new applications introduced to the Android market has skyrocketed. The recent appearance of a wide range of mobile malware has caught the attention of security professionals and scholars alike. In light of the ongoing expansion of the mobile phone industry, the likelihood of it being used in criminal activities will only continue to rise in the future. This article reviews the literature on malware detection and prevention in Android mobile devices, analyzes the existing literature on major studies and tasks, and covers articles, journals, and digital resources such as Internet security publications, scientific studies, and conferences.
References
[1]
Rastogi, S., Bhushan, K. and Gupta, B.B. (2016) Android Applications Repackaging Detection Techniques for Smartphone Devices. ProcediaComputerScience, 78, 26-32. https://doi.org/10.1016/j.procs.2016.02.006
[2]
Xu, C. (n.d.) Android Ransomware Trends and Case Studies: A Reverse Engineering Approach. 55.
[3]
Pandela, T. and Riadi, I. (2020) Browser Forensics on Web-Based Tiktok Applications. InternationalJournalofComputerApplications, 175, 47-52. https://doi.org/10.5120/ijca2020920897
[4]
Goni, I., Gumpy, J.M., Maigari, T.U. and Mohammad, M. (2020) Cybersecurity and Cyber Forensics: Machine Learning Approach Systematic Review. SemiconductorScienceandInformationDevices, 2, 25-29. https://doi.org/10.30564/ssid.v2i2.2495
[5]
Wu, Q., Zhu, X. and Liu, B. (2021) A Survey of Android Malware Static Detection Technology Based on Machine Learning. MobileInformationSystems, 2021, Article ID: 8896013. https://doi.org/10.1155/2021/8896013
[6]
Amro, B. (2017) Malware Detection Techniques for Mobile Devices. InternationalJournalofMobileNetworkCommunications&Telematics, 7, 1-10. https://doi.org/10.5121/ijmnct.2017.7601
[7]
Kaushik, P. and Jain, A. (2015) Malware Detection Techniques in Android. InternationalJournalofComputerApplications, 122, 22-26. https://doi.org/10.5120/21794-5166
[8]
Gonzalez, H., Kadir, A.A., Stakhanova, N., Alzahrani, A.J. and Ghorbani, A.A. (2015) Exploring Reverse Engineering Symptoms in Android Apps. Proceedingsofthe 8thEuropeanWorkshoponSystemSecurity, Bordeaux, 21 April 2015, 1-7. https://doi.org/10.1145/2751323.2751330
[9]
Gope, P. (2019) LAAP: Lightweight Anonymous Authentication Protocol for D2D-Aided Fog Computing Paradigm. Computers&Security, 86, 223-237. https://doi.org/10.1016/j.cose.2019.06.003
[10]
Amro, B. and Abu Znaid, Z. (2021) User Centric Android Application Permission Manager. Al-RafidainJournalofComputerSciencesandMathematics, 15, 213-223. https://doi.org/10.33899/csmj.2021.170043
[11]
OWASP Top Ten 2017|2017 Top 10|OWASP Foundation (n.d.). https://owasp.org/www-project-top-ten/2017/Top_10
Introduction—OWASP Top 10 Proactive Controls (n.d.). https://top10proactive.owasp.org/archive/2018/0x04-introduction/#:~:text=The%20OWASP%20Top%20Ten%20Proactive,those%20new%20to%20secure%20development
[14]
Alkindi, Z.R., Sarrab, M. and Alzeidi, N. (2021) User Privacy and Data Flow Control for Android Apps: Systematic Literature Review. JournalofCyberSecurityandMobility, 10, 261-304. https://doi.org/10.13052/jcsm2245-1439.1019
[15]
Darabian, H., Homayounoot, S., Dehghantanha, A., Hashemi, S., Karimipour, H., Parizi, R.M., et al. (2020) Detecting Cryptomining Malware: A Deep Learning Approach for Static and Dynamic Analysis. JournalofGridComputing, 18, 293-303. https://doi.org/10.1007/s10723-020-09510-6
[16]
Alanda, A., Satria, D., Mooduto, H.A. and Kurniawan, B. (2020) Mobile Application Security Penetration Testing Based on OWASP. IOPConferenceSeries: MaterialsScienceandEngineering, 846, Article ID: 012036. https://doi.org/10.1088/1757-899x/846/1/012036
[17]
Feng, R., Chen, S., Xie, X., Meng, G., Lin, S. and Liu, Y. (2021) A Performance-Sensitive Malware Detection System Using Deep Learning on Mobile Devices. IEEETransactionsonInformationForensicsandSecurity, 16, 1563-1578. https://doi.org/10.1109/tifs.2020.3025436
[18]
Narwal, B. and Goel, N. (2020) A Walkthrough of Digital Forensics and Its Tools.
[19]
Babun, L., Sikder, A.K., Acar, A. and Uluagac, A.S. (2019) A Digital Forensics Framework for Smart Settings. Proceedingsofthe 12thConferenceonSecurityandPrivacyinWirelessandMobileNetworks, Miami, 15-17 May 2019, 332-333. https://doi.org/10.1145/3317549.3326317
[20]
Surendran, R., Thomas, T. and Emmanuel, S. (2020) A TAN Based Hybrid Model for Android Malware Detection. JournalofInformationSecurityandApplications, 54, Article ID: 102483. https://doi.org/10.1016/j.jisa.2020.102483
[21]
Zhao, Y., Tang, Z., Ye, G., Peng, D., Fang, D., Chen, X., et al. (2020) Compile-Time Code Virtualization for Android Applications. Computers&Security, 94, Article ID: 101821. https://doi.org/10.1016/j.cose.2020.101821
[22]
Gyunka, B.A. and Barda, S.I. (2020) Anomaly Detection of Android Malware Using One-Class K-Nearest Neighbours (OC-KNN). NigerianJournalofTechnology, 39, 542-552. https://doi.org/10.4314/njt.v39i2.25
[23]
Kapratwar, A., Di Troia, F. and Stamp, M. (2017) Static and Dynamic Analysis of Android Malware. Proceedingsofthe 3rdInternationalConferenceonInformationSystemsSecurityandPrivacy, Vol. 1, 653-662. https://doi.org/10.5220/0006256706530662
[24]
Ahsen, M., Hassan, S.A. and Jayakody, D.N.K. (2016) Propagation Modeling in Large-Scale Cooperative Multi-Hop Ad Hoc Networks. IEEEAccess, 4, 8925-8937. https://doi.org/10.1109/access.2016.2635718
[25]
Egele, M., Kruegel, C., Kirda, E. and Vigna, G. (n.d.) PiOS: Detecting Privacy Leaks in iOS Applications.
[26]
Alashjaee, A.M. and Haney, M. (2021) Forensic Requirements Specification for Mobile Device Malware Forensic Models. 2021 IEEE 11thAnnualComputingandCommunicationWorkshopandConference (CCWC), 27-30 January 2021, 930-935. https://doi.org/10.1109/ccwc51732.2021.9376043
[27]
Sharma, B.K., Joseph, M.A., Jacob, B. and Miranda, B. (2019) Emerging Trends in Digital Forensic and Cyber Security—An Overview. 2019 6thHCTInformationTechnologyTrends (ITT), Ras Al Khaimah, 20-21 November 2019, 309-313. https://doi.org/10.1109/itt48889.2019.9075101
[28]
Alzaylaee, M.K., Yerima, S.Y. and Sezer, S. (2020) Dl-Droid: Deep Learning Based Android Malware Detection Using Real Devices. Computers&Security, 89, Article ID: 101663. https://doi.org/10.1016/j.cose.2019.101663
[29]
You, G., Kim, G., Cho, S. and Han, H. (2021) A Comparative Study on Optimization, Obfuscation, and Deobfuscation Tools in Android. JournalofInternetServicesandInformationSecurity, 11, 2-15. https://doi.org/10.22667/JISIS.2021.02.28.002
