Abstract
Malware for Android is becoming increasingly dangerous to the safety of mobile devices and the data they hold. Although machine learning (ML) techniques have been shown to be effective at detecting malware for Android, a comprehensive analysis of the methods used is required. We review the current state of Android malware detection using machine learning in this paper. We begin by providing an overview of Android malware and the security issues it causes. Then, we look at the various supervised, unsupervised, and deep learning, machine learning approaches that have been utilized for Android malware detection. Additionally, we present a comparison of the performance of various Android malware detection methods and talk about the performance evaluation metrics that are utilized to evaluate their efficacy. Finally, we draw attention to the drawbacks and difficulties of the methods that are currently in use and suggest possible future directions for research in this area. In addition to providing insights into the current state of Android malware detection using machine learning, our review provides a comprehensive overview of the subject.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Mahindru, A., Sangal, A.L.: MLDroid-framework for Android malware detection using machine learning techniques. Neural Comput. Appl. 33, 5183–5240 (2021)
Mahindru, A., Singh, P.: Dynamic permissions based android malware detection using machine learning techniques. In: Proceedings of the 10th Innovations in Software Engineering Conference (ISEC ’17). Association for Computing Machinery, New York, NY, USA, pp. 202–210 (2017). https://doi.org/10.1145/3021460.3021485
Zhou, Y., Wang, Z., Zhou, W., Jiang, X.: Hey, you, get off of my market: detecting malicious apps in official and alternative Android markets. In: Proceedings of the 19th Annual Network & Distributed System Security Symposium (2012)
Zhou, Y., Jiang, X.: Dissecting android Malware: characterization and evolution security and privacy (SP). In: 2012 IEEE Symposium on Security and Privacy (2012)
Cheng, J., Wong, S.H., Yang, H., Lu, S.: SmartSiren: virus detection and alert for smartphones. In: International Conference on Mobile Systems, Applications, and Services (MobiSys) (2007)
Sanz, B., Santos, I., Laorden, C., Ugarte-Pedrero, X., Bringas, P.G., Alvarez, G.: PUMA: permission usage to detect Malware in Android. In: Advances in Intelligent Systems and Computing (AISC) (2012)
Wang, J., Deng, P., Fan, Y., Jaw, L., Liu, Y.: Virus detection using data mining techniques. In: Proceedings of IEEE International Conference on Data Mining (2003)
Chen, X., Andersen, J., Mao, Z., Bailey, M., Nazario, J.: Towards an understanding of anti-virtualization and anti-debugging behavior in modern malware. In: DSN (2008)
Jidigam, R.K., Austin, T.H., Stamp, M.: Singular value decomposition and metamorphic detection. J. Comput. Virol. Hacking Tech. 11(4), 203–216 (2014). https://doi.org/10.1007/s11416-014-0220-0
Fredrikson, M., Jha, S., Christodorescu, M., Sailer, R., Yan, X.: Synthesizing near-optimal malware specifications from suspicious behaviors. In: SP 2010 Proceedings of the 2010 IEEE Symposium on Security and Privacy, pp. 45–60 (2010)
Kolbitsch, C., Comparetti, P.M., Kruegel, C., Kirda, E., Zhou, X., Wang, X.: Effective and efficient malware detection at the end host. In: USENIX Security (2009)
Lanzi, A., Balzarotti, D., Kruegel, C., Christodorescu, M., Kirda, E.: AccessMiner: using system-centric models for malware protection. In: CCS (2010)
Arai, K., Bhatia, R. (eds.): FICC 2019. LNNS, vol. 70. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-12385-7
Chowdhury, M.N.-U.-R., Alahy, Q.E., Soliman, H.: Advanced android malware detection utilizing API calls and permissions. In: Kim, H., Kim, K.J. (eds.) IT Convergence and Security. LNEE, vol. 782, pp. 123–134. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-4118-3_12
Lu, T., Du, Y., Ouyang, L., Chen, Q., Wang, X.: Android malware detection based on a hybrid deep learning model. Secur. Commun. Netw. 2020, Article ID 8863617, 11 p. (2020)
Kim, J., Ban, Y., Ko, E., et al.: MAPAS: a practical deep learning-based android malware detection system. Int. J. Inf. Secur. 21, 725–738 (2022)
MARVIN: Efficient and Comprehensive Mobile App Classification through Static and Dynamic Analysis
Virus Total. http://www.virustotal.com/gui/graph-overview
Zhou, Y., Wang, Z., Zhou, W., Jiang, X.: Hey, you, get off of my market: detecting malicious apps in official and alternative Android markets. In: Proceedings of the 19th Annual Network & Distributed System Security Symposium (2012)
Zhou, Y., Jiang, X.: Dissecting android malware: characterization and evolution security and privacy (SP). In: 2012 IEEE Symposium on Security and Privacy
Arp, D., Spreitzenbarth, M., Huebner, M., Gascon, H., Rieck, K.: Drebin: efficient and explainable detection of android malware in your pocket. In: 21st Annual Network and Distributed System Security Symposium (NDSS) (2014)
Allix, K., Bissyandé, T.F., Klein, J., Traon, Y.L.: AndroZoo: collecting millions of android apps for the research community. In: 2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR), Austin, TX, USA, pp. 468–471 (2016)
Liu, Z., Wang, R., Japkowicz, N., Tang, D., Zhang, W., Zhao, J.: Research on unsupervised feature learning for Android malware detection based on restricted Boltzmann machines. Future Gener. Comput. Syst. 120, pp. 91–108 (2021). ISSN 0167–739X. https://doi.org/10.1016/j.future.2021.02.015
Liu, Z.-L., Yang, M., Chen, X., Luo, Y., Zhang, H.: An android malware detection model based on DT-SVM. Secur. Commun. Netw. (2020). https://doi.org/10.1155/2020/8841233
AlJarrah, M.N., Yaseen, Q.M., Mustafa, A.M.: a context-aware android malware detection approach using machine learning. Information 13(12), 563 (2022). https://doi.org/10.3390/info13120563
Jeon, S., Moon, J.: Malware-detection method with a convolutional recurrent neural network using opcode sequences. Inf. Sci. 535, 1–15 (2020). ISSN 0020–0255. https://doi.org/10.1016/j.ins.2020.05.026
Lee, J., Jang, H., Ha, S., Yoon, Y.: Android malware detection using machine learning with feature selection based on the genetic algorithm. Mathematics. 9(21), 2813 (2021). https://doi.org/10.3390/math9212813
Kwon, H.-Y., Kim, T., Lee, M.-K.: Advanced intrusion detection combining signature-based and behavior-based detection methods. Electronics 11(6), 867 (2022). https://doi.org/10.3390/electronics11060867
Pulver, A., Lyu, S.: LSTM with working memory. In: 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA, pp. 845–851. https://doi.org/10.1109/IJCNN.2017.7965940
Alzubaidi, L., Zhang, J., Humaidi, A.J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M.A., Al-Amidie, M., Farhan, L.: Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J. Big Data 8(1), 1–74 (2021). https://doi.org/10.1186/s40537-021-00444-8
Yahya, A.E., Gharbi, A., Yafooz, W.M.S., Al-Dhaqm, A.: A novel hybrid deep learning model for detecting and classifying non-functional requirements of mobile apps issues. Electronics 12(5), 1258 (2023). https://doi.org/10.3390/electronics12051258
Liu, K., Xu, S., Xu, G., Zhang, M., Sun, D., Liu, H.: A review of android malware detection approaches based on machine learning. IEEE Access 8, 124579–124607 (2020). https://doi.org/10.1109/ACCESS.2020.3006143
Cheng, J., Wong, S.H., Yang, H., Lu, S.: SmartSiren: virus detection and alert for smartphones. In: Proceedings of the 5th international conference on Mobile systems, applications and services (MobiSys ’07). Association for Computing Machinery, New York, NY, USA, pp. 258–271 (2007). https://doi.org/10.1145/1247660.1247690
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chowdhury, NUR., Haque, A., Soliman, H., Hossen, M.S., Fatima, T., Ahmed, I. (2024). Android Malware Detection Using Machine Learning: A Review. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2023. Lecture Notes in Networks and Systems, vol 824. Springer, Cham. https://doi.org/10.1007/978-3-031-47715-7_35
Download citation
DOI: https://doi.org/10.1007/978-3-031-47715-7_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-47714-0
Online ISBN: 978-3-031-47715-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)