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Learning-based Android malware detectors degrade over time due to natural distribution drift caused by malware variants and new families.
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L. Xue, H. Zhou, X. Luo, Y. Zhou, Y. Shi, G. Gu, F. Zhang, and M. H. Au, “Happer: Unpacking android apps via a hardware-assisted approach,” in 2021 IEEE Symposium on Security and Privacy (SP) . IEEE, 2021, pp. 1641–1658
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2021
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T. Chow, Z. Kan, L. Linhardt, L. Cavallaro, D. Arp, and F. Pierazzi, “Drift forensics of malware classifiers,” in Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security , 2023, pp. 197–207
2023
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T. Lu and J. Wang, “Domr: Towards deep open-world malware recognition,” IEEE Transactions on Information Forensics and Security , 2023
2023
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Y. Chen, Z. Ding, and D. Wagner, “Continuous learning for android malware detection,” in 32nd USENIX Security Symposium (USENIX Security 23) , 2023, pp. 1127–1144
2023
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N. Daoudi, K. Allix, T. F. Bissyandé, and J. Klein, “Guided retraining to enhance the detection of difficult android malware,” in Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis , 2023, pp. 1131–1143
2023
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K. Pei, W. Li, Q. Jin, S. Liu, S. Geng, L. Cavallaro, J. Yang, and S. Jana, “Exploiting code symmetries for learning program semantics,” in Forty-first International Conference on Machine Learning (Spotlight) , 2024
2024
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Y. Yang, B. Yuan, J. Lou, and Z. Qin, “Scrr: Stable malware detection under unknown deployment environment shift by decoupled spurious correlations filtering,” IEEE Transactions on Dependable and Secure Computing , 2024
2024
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M. Yang et al. , “Invariant learning via probability of sufficient and necessary causes,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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2024
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Y. Chen, W. Huang, K. Zhou, Y. Bian, B. Han, and J. Cheng, “Understanding and improving feature learning for out-of-distribution generalization,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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2024
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T. Sun, N. Daoudi, W. Pian, K. Kim, K. Allix, T. F. Bissyande, and J. Klein, “Temporal-incremental learning for android malware detection,” ACM Transactions on Software Engineering and Methodology , vol. 34, no. 4, pp. 1–30, 2025
2025
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