From Grim Reality to Practical Solution: Malware Classification in Real-World Noise. Abstract: Malware datasets inevitably contain incorrect labels due to ...
Since our goal is to correctly classify malware families rather than con- ducting anomaly detection where datasets have many more benign samples than malicious ...
From Grim Reality to Practical Solution: Malware Classification in Real-World Noise. 2023, pp. 2602-2619,. DOI Bookmark: 10.1109/SP46215.2023.10179453.
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Jan 16, 2024 · From Grim Reality to Practical Solution: Malware Classification in Real-World Noise ... malware analysis is the classification of malware ...
This dataset contains 4,683 benign ware and 16,706 android malware, coming from 12 families (see Table 2). It is extremely hard to manually check the label ...
This repo contains the code for the S&P 23 paper titled "From Grim Reality to Practical Solution: Malware Classification in Real-World Noise".
From Grim Reality to Practical Solution: Malware Classification in Real-World Noise Xian Wu, Wenbo Guo, Jia Yan, Baris Coskun, Xinyu Xing In IEEE Symposium ...
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From grim reality to practical solution: Malware classification in real-world noise. X Wu, W Guo, J Yan, B Coskun, X Xing. 2023 IEEE Symposium on Security and ...
Wu, X., Guo, W., Yan, J., Coskun, B., Xing, X., "From Grim Reality to Practical Solution: Malware Classification in Real-World Noise", Proceedings of the ...