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Review on Android Malware Detection System

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Data Management, Analytics and Innovation (ICDMAI 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 137))

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Abstract

Modern mobile devices have become important part of day to day life as they offer various tools and services which are very useful. Mobile devices handle lot of sensitive information hence malware writers try to exploit vulnerabilities to gain access. Android is one of the most popular OS for mobile device. The open source nature of Android Operating System has attracted users because of open source nature and large number of users it has become lucrative for malware writers to target android devices. With time there is continuous evolution in variety, volume and sophistication of malwares. Researchers are continuously working to develop better malware detection methods which can deal with sophisticated malwares. This paper presents comprehensive overview of latest malware detection methods. The rapid increase of mobile devices and android operating system has led to security issues with android operating system. Malware writers attracted with the increasing use of smart phones and android OS. Hence there is a need to study existing malware detection system for android OS. This paper gives the detailed review on android malware detection system.

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References

  1. J. Li, L. Sun, Q. Yan, Z. Li, W. Srisa-an, H. Ye, Significant permission identification for machine-learning-based android Malware detection. IEEE Trans. Ind. Inf. 14(7), 3216–3225 (2018). https://doi.org/10.1109/TII.2017.2789219

    Article  Google Scholar 

  2. O. Aslan, R. Samet, A comprehensive review on malware detection approaches. IEEE Access 8, 6249–6271 (2020). https://doi.org/10.1109/ACCESS.2019.2963724

    Article  Google Scholar 

  3. F. Alswaina, K. Elleithy, Android malware family classification and analysis: current status and future directions. Electronics 9(6), 942 (2020)

    Article  Google Scholar 

  4. Understanding Android Malware Families. https://www.itworldcanada.com/blog/understanding-android-malware-families-uamf-the-foundations-article-1/441562#:~:text=The%20prominent%20Android%20malware%20categories,banker%2C%20and%20trojan%2Ddropper. Accessed on 13 May 2021

  5. B. Amro, Malware detection techniques for mobile devices. Int. J. Mob. Netw. Commun. Telemat. (IJMNCT) 7 (2017)

    Google Scholar 

  6. A. Damodaran, F. Di Troia, C.A. Visaggio, T.H. Austin, M. Stamp, A comparison of static, dynamic, and hybrid analysis for malware detection. J. Comput. Virol. Hack. Tech. 13(1), 1–12 (2017)

    Article  Google Scholar 

  7. A. Demontis et al., Yes, machine learning can be more secure! a case study on android malware detection. IEEE Trans. Dependable Secure Comput. 16(4), 711–724 (2017)

    Article  Google Scholar 

  8. D. Arp, M. Spreitzenbarth, M. Hubner, H. Gascon, K. Rieck, C. Siemens, Drebin: effective and explainable detection of android malware in your pocket. NDSS 14, 23–26 (2014)

    Google Scholar 

  9. W. Enck et al., Taintdroid: an information-flow tracking system for realtime privacy monitoring on smartphones. ACM Trans. Comput. Syst. (TOCS) 32(2), 1–29 (2014)

    Article  Google Scholar 

  10. T. Bläsing, L. Batyuk, A.-D. Schmidt, S.A. Camtepe, S. Albayrak, An android application sandbox system for suspicious software detection, in 2010 5th International Conference on Malicious and Unwanted Software (2010), pp. 55–62

    Google Scholar 

  11. A. Shabtai, U. Kanonov, Y. Elovici, C. Glezer, Y. Weiss, ‘Andromaly’: a behavioral malware detection framework for android devices. J. Intell. Inf. Syst. 38(1), 161–190 (2012). https://doi.org/10.1007/s10844-010-0148-x

    Article  Google Scholar 

  12. A. Saracino, D. Sgandurra, G. Dini, F. Martinelli, MADAM: effective and efficient behavior-based android malware detection and prevention. IEEE Trans. Dependable Secure Comput. 15(1), 83–97 (2018). https://doi.org/10.1109/TDSC.2016.2536605

    Article  Google Scholar 

  13. E. Gandotra, D. Bansal, S. Sofat, Malware analysis and classification: a survey. J. Inf. Secur. 5(2), 56–64 (2014)

    Google Scholar 

  14. L.D. Coronado-De-Alba, A. Rodriguez-Mota, P.J. Escamilla-Ambrosio, Feature selection and ensemble of classifiers for Android malware detection, in 2016 8th IEEE Latin-American Conference on Communications (LATINCOM). Medellin, Nov 2016, pp. 1–6. https://doi.org/10.1109/LATINCOM.2016.7811605

  15. A. Feizollah, N.B. Anuar, R. Salleh, A.W.A. Wahab, A review on feature selection in mobile malware detection. Digit. Investig. 13, 22–37 (2015). https://doi.org/10.1016/j.diin.2015.02.001

    Article  Google Scholar 

  16. H.-H. Kim, M.-J. Choi, Linux kernel-based feature selection for Android malware detection, in The 16th Asia-Pacific Network Operations and Management Symposium. Hsinchu, Sept 2014, pp. 1–4. https://doi.org/10.1109/APNOMS.2014.6996540

  17. M.Z. Mas’ud, S. Sahib, M.F. Abdollah, S.R. Selamat, R. Yusof, Analysis of features selection and machine learning classifier in android malware detection, in 2014 International Conference on Information Science and Applications (ICISA). Seoul, South Korea, May 2014, pp. 1–5. https://doi.org/10.1109/ICISA.2014.6847364

  18. J. Ribeiro, F.B. Saghezchi, G. Mantas, J. Rodriguez, R.A. Abd-Alhameed, HIDROID: prototyping a behavioral host-based intrusion detection and prevention system for android. IEEE Access 8, 23154–23168 (2020). https://doi.org/10.1109/ACCESS.2020.2969626

    Article  Google Scholar 

  19. H. Cai, N. Meng, B. Ryder, D. Yao, Droidcat: effective android malware detection and categorization via app-level profiling. IEEE Trans. Inf. Forensics Secur. 14(6), 1455–1470 (2018)

    Article  Google Scholar 

  20. S. Badhani, S.K. Muttoo, CENDroid—a cluster-ensemble classifier for detecting malicious android applications. Comput. Secur. 85, 25–40 (2019)

    Article  Google Scholar 

  21. H. Han, S. Lim, K. Suh, S. Park, S. Cho, M. Park, Enhanced android malware detection: an SVM-based machine learning approach, in 2020 IEEE International Conference on Big Data and Smart Computing (BigComp) (2020), pp. 75–81

    Google Scholar 

  22. K. Tian, D. Yao, B.G. Ryder, G. Tan, G. Peng, Detection of repackaged android malware with code-heterogeneity features. IEEE Trans. Dependable Secure Comput. 17(1), 64–77 (2017)

    Article  Google Scholar 

  23. T. Kim, B. Kang, M. Rho, S. Sezer, E.G. Im, A multimodal deep learning method for android malware detection using various features. IEEE Trans. Inf. Forensics Secur. 14(3), 773–788 (2018)

    Article  Google Scholar 

  24. Z. Aung, W. Zaw, Permission-based android malware detection. Int. J. Sci. Technol. Res. 2(3), 228–234 (2013)

    Google Scholar 

  25. A. Feizollah et al., A review on feature selection in mobile malware detection. Digital Invest. 13(2015), 22–37

    Google Scholar 

  26. K. Liu et al., A review of android malware detection approaches based on machine learning. IEEE Access 8, 124579–124607 (2020)

    Google Scholar 

  27. Z. Bazrafshan et al., A survey on heuristic malware detection techniques, in The 5th Conference on Information and Knowledge Technology (IEEE, 2013).

    Google Scholar 

  28. P. Vinod et al., Survey on malware detection methods, in Proceedings of the 3rd Hackers’ Workshop on Computer and Internet Security (IITKHACK’09) (2009)

    Google Scholar 

  29. M. Damshenas, M0droid: an android behavioral-based malware detection model. J. Inf. Priv. Secur. 11(3), 141–157 (2015)

    Google Scholar 

  30. M. Zheng, S. Min, J.C.S. Lui, Droid analytics: a signature based analytic system to collect, extract, analyze and associate android malware, in 2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE, 2013).

    Google Scholar 

  31. P. Feng, J. Ma, C. Sun, X. Xu, Y. Ma, A novel dynamic android malware detection system with ensemble learning. IEEE Access 6, 30996–31011 (2018). https://doi.org/10.1109/ACCESS.2018.2844349

    Article  Google Scholar 

  32. Z.-U. Rehman et al., Machine learning-assisted signature and heuristic-based detection of malwares in android devices. Comput. Electr. Eng. 69, 828–841 (2018)

    Google Scholar 

  33. G. Nguyen et al., A heuristics approach to mine behavioural data logs in mobile malware detection system. Data Knowl. Eng. 115 (2018), 129–151

    Google Scholar 

  34. S.Y. Yerima, S. Sezer, I. Muttik, High accuracy android malware detection using ensemble learning. IET Inf. Secur. 9(6), 313–320 (2015)

    Article  Google Scholar 

  35. T. Petsas et al., Rage against the virtual machine: hindering dynamic analysis of android malware, in Proceedings of the Seventh European Workshop on System Security (2014)

    Google Scholar 

  36. S.Y. Yerima, S. Sezer, I. Muttik, Android malware detection using parallel machine learning classifiers, in 2014 Eighth International Conference on Next Generation Mobile Apps, Services and Technologies. Oxford, United Kingdom, Sept 2014, pp. 37–42. https://doi.org/10.1109/NGMAST.2014.23

  37. E.G. Dada et al. Performance evaluation of machine learning algorithms for detection and prevention of malware attacks. IOSR J. Comput. Eng. 21(3), 18–27 (2019)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, PAHSUS, Solapur, India

    Rajeshwari Gundla

  2. Department of Electronics Engineering, Walchand Institute of Technology, Solapur, India

    Sachin R. Gengaje

Authors
  1. Rajeshwari Gundla
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  2. Sachin R. Gengaje
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Corresponding author

Correspondence to Rajeshwari Gundla .

Editor information

Editors and Affiliations

  1. Bangabasi Morning College, Kolkata, West Bengal, India

    Saptarsi Goswami

  2. Vara Technology, Saket, Delhi, India

    Inderjit Singh Barara

  3. Society for Data Science, Pune, Maharashtra, India

    Amol Goje

  4. National University of Singapore, Singapore, Singapore

    C. Mohan

  5. Department of Computer Science, Technion—Israel Institute of Technology, Haifa, Israel

    Alfred M. Bruckstein

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Gundla, R., Gengaje, S.R. (2023). Review on Android Malware Detection System. In: Goswami, S., Barara, I.S., Goje, A., Mohan, C., Bruckstein, A.M. (eds) Data Management, Analytics and Innovation. ICDMAI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 137. Springer, Singapore. https://doi.org/10.1007/978-981-19-2600-6_6

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