Malicious Adware Detection on Android Platform using Dynamic Random Forest

Author:

Lee Kyungmin,Park Hyunhee

Publisher

Springer International Publishing

Reference9 articles.

1. Sun, C., Zhang, H., Qin, S., He, N., Qin, J., Pan, H.: DexX: a double layer unpacking framework for Android. IEEE J. Mag. 6, 61267–61276 (2018)

2. Xiong, P., Wang, X., Niu, W., Zhu, T., Li, G.: Android malware detection with contrasting permission patterns. China Commun. 11(8), 1–14 (2014)

3. Faruki, P., Bharmal, A., Laxmi, V., Ganmoor, V., Gaur, M.S., Conti, M., Rajarajan, M.: Android security: a survey of issues, malware penetration, and defenses. IEEE Commun. Surv. Tutor. 17(2), 998–1022 (2015)

4. Wan, W., Gao, Z., Zhao, M., Li, Y., Liu, J., Zhang, X.: DroidEnsemble: detecting Android malicious applications with ensemble of string and structural static features. IEEE Access 6, 31798–31807 (2018)

5. Zhou, Y., Jiang, X.: Dissecting Android malware: characterization and evolution. In: Proceedings of the 2012 IEEE Symposium on Security and Privacy. San Francisco, CA, USA (2012)

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. SafeDroid: Safeguarding Android Mobile Phones from Adware and Banking Maldroid Attacks;2023 International Conference on Sustainable Communication Networks and Application (ICSCNA);2023-11-15

2. MadDroid: malicious adware detection in Android using deep learning;Journal of Cyber Security Technology;2023-08-22

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