73 citations · 74 across the 5 of their papers we have counts for
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cs.LG2024★ 1 cited
On the Effectiveness of Adversarial Training on Malware Classifiers
Hamid Bostani, Jacopo Cortellazzi, Daniel Arp +3
Adversarial Training (AT) is a key defense against Machine Learning evasion attacks, but its effectiveness for real-world malware detection remains poorly understood. This uncertai…
cs.CR2024
Improving Adversarial Robustness in Android Malware Detection by Reducing the Impact of Spurious Correlations
Hamid Bostani, Zhengyu Zhao, Veelasha Moonsamy
Machine learning (ML) has demonstrated significant advancements in Android malware detection (AMD); however, the resilience of ML against realistic evasion attacks remains a major…