FGFDect: A Fine-Grained Features Classification Model for Android Malware Detection
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Publisher
Springer International Publishing
Link
http://link.springer.com/content/pdf/10.1007/978-3-030-01701-9_16
Reference19 articles.
1. Chen, K., Wang, X., Chen, Y., et al.: Following devil’s footprints: cross-platform analysis of potentially harmful libraries on android and iOS. In: 2016 IEEE Symposium on Security and Privacy (SP), pp. 357–376. IEEE (2016)
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3. Li, L., Bissyandé, T.F., Octeau, D., et al.: DroidRa: taming reflection to support whole-program analysis of android apps. In: Proceedings of the 25th International Symposium on Software Testing and Analysis, pp. 318–329. ACM (2016)
4. Rastogi, V., Chen, Y., Jiang, X.: DroidChameleon: evaluating android anti-malware against transformation attacks. In: Proceedings of the 8th ACM SIGSAC Symposium on Information, Computer and Communications Security, pp. 329–334. ACM (2013)
5. Yuan, Z., Lu, Y., Xue, Y.: DroidDetector: android malware characterization and detection using deep learning. J. Tsinghua Univ. (Sci. Technol.) 21(1), 114–123 (2016)
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1. Malicious application detection in android — A systematic literature review;Computer Science Review;2021-05
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