Abstract
Android has the biggest worldwide market share owing to its open-source nature and Google’s support. Since it is the most broadly utilized working framework in the world, it has attracted the attention of cyber criminals who use it to spread malware. Using an evolving genetic algorithm for feature selection, the researchers developed an Android malware detection machine-learning approach that relies on machine learning. Machine-learning classifiers are trained using chosen features from the Genetic algorithm, and their ability to recognize malware is assessed when include choice. According to the trials, the Genetic algorithm gives the most efficient feature subset, enabling the feature dimension to be decreased by half from the original feature set. After feature selection, machine learning–based classifiers retain a classification accuracy of more than 94 percent despite operating on a significantly smaller feature dimension, reducing computing cost of learning classifiers.
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References
Li J, Sun L, Yan Q, Li Z, Srisa-An W, Ye H (2018) Significant permission identification for machine-learning-based Android malware detection. IEEE Trans Ind Inform 14(7):3216–3225
Arshad S, Shah MA, Wahid A, Mehmood A, Song H, Yu H (2018) SAMADroid: a novel 3-level hybrid malware detection model for Android operating system. IEEE Access 6:4321–4339
Kim T, Kang B, Rho M, Sezer S, Im EG (2018) A multimodal deep learning method for android malware detection using various features. 6013(c)
Saracino A, Sgandurra D, Dini G, Martinelli F (2018) MADAM: effective and efficient behavior-based Android malware detection and prevention. IEEE Trans Dependable Secur Comput 15(1):83–97
Kim T, Kang B, Rho M, Sezer S, Im EG (2018) A multimodal deep learning method for android malware detection using various features. 6013(c)
Firdaus A, Anuar NB, Karim A, Faizal M, Razak A (2018) Discovering optimal features using static analysis and a genetic search-based method for Android malware detection *. 19(6):712–736
Chebyshev V (2021) Mobile Malware Evolution 2020, 1 March 2021. Available online: https://securelist.com/mobile-malware-evolution-2020/101029/. Accessed on 7 May 2021
StatCounter. Mobile operating system market share worldwide, May 2021. Available online: https://gs.statcounter.com/os-market-share/mobile/worldwide. Accessed on 10 June 2021
Liu K, Xu S, Xu G, Zhang M, Sun D, Liu H (2020) A review of Android malware detection approaches based on machine learning. IEEE Access 8:124579–124607. [Google Scholar] [CrossRef]
Wang Z, Liu Q, Chi Y (2020) Review of Android malware detection based on deep learning. IEEE Access 8:181102–181126
Rana MS, Gudla C, Sung AH (2018) Evaluating machine learning models for Android malware detection: a comparison study. In: Proceedings of the 2018 VII international conference on network, communication and computing, Taipei City, Taiwan, 14–16 Dec 2018, pp 17–21
Mahindru A, Sangal A (2021) MLDroid—framework for Android malware detection using machine learning techniques. Neural Comput Appl 33:5183–5240
Şahin DÖ, Kural OE, Akleylek S, Kılıç E (2021) A novel Android malware detection system: adaption of filter-based feature selection methods. J Ambient Intell Humaniz Comput 15:1–15
Firdaus A, Anuar NB, Karim A, Ab Razak MF (2018) Discovering optimal features using static analysis and a genetic search-based method for Android malware detection. Front Inf Technol Electron Eng 19:712–736. [Google Scholar]
Fatima A, Maurya R, Dutta MK, Burget R, Masek J (2019) Android malware detection using genetic algorithm based optimized feature selection and machine learning. In: Proceedings of the 2019 42nd international conference on telecommunications and signal processing (TSP), Budapest, Hungary, 1–3 July 2019, pp 220–223
Yildiz O, Doğru IA (2019) Permission-based android malware detection system using feature selection with genetic algorithm. Int J Softw Eng Knowl Eng 29:245–262
Meimandi A, Seyfari Y, Lotfi S (2020) Android malware detection using feature selection with hybrid genetic algorithm and simulated annealing. In: Proceedings of the 2020 IEEE 5th conference on technology in electrical and computer engineering (ETECH 2020) information and communication technology (ICT), Tehran, Iran, 22 Oct 2020
Wang J, Jing Q, Gao J, Qiu X (2020) SEdroid: a robust Android malware detector using selective ensemble learning. In: Proceedings of the 2020 IEEE wireless communications and networking conference (WCNC), Seoul, Korea, 25–28 May 2020, pp 1–5
Wang L, Gao Y, Gao S, Yong X (2021) A new feature selection method based on a self-variant genetic algorithm applied to Android malware detection. Symmetry 13:1290
Yen YS, Sun HM (2019) An Android mutation malware detection based on deep learning using visualization of importance from codes. Microelectron Reliab 93:109–114
Lim K, Kim NY, Jeong Y, Cho S, Han S, Park M (2019) Protecting Android applications with multiple DEX files against static reverse engineering attacks. Intell Autom Soft Comput 25:143–154
Lee SJ, Moon HJ, Kim DJ, Yoon Y (2019) Genetic algorithm-based feature selection for depression scale prediction. In: Proceedings of the ACM GECCO conference, Prague, Czech Republic, 13–17 July 2019, pp 65–66. [Google Scholar]
Lambora A, Gupta K, Chopra K (2019) Genetic algorithm—a literature review. In: Proceedings of the 2019 international conference on machine learning, big data, cloud and parallel computing (COMITCon), Faridabad, India, 14–16 Feb 2019, pp 380–384. [Google Scholar]
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M. Sonia, Lakshmi, C.B.N., Hussain, S.J., Swarupa, M.L., N. Rajeswaran (2024). Android Malware Detection Using Genetic Algorithm Based Optimized Feature Selection and Machine Learning. In: Gunjan, V.K., Kumar, A., Zurada, J.M., Singh, S.N. (eds) Computational Intelligence in Machine Learning. ICCIML 2022. Lecture Notes in Electrical Engineering, vol 1106. Springer, Singapore. https://doi.org/10.1007/978-981-99-7954-7_19
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