153 citations · 338 across the 7 of their papers we have counts for
Showing 2020Show all
3 papers · 1 filter
cs.CR2020★ 153 cited
Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware Detection
Deqiang Li, Qianmu Li
Malware remains a big threat to cyber security, calling for machine learning based malware detection. While promising, such detectors are known to be vulnerable to evasion attacks.…
cs.CR2020★ 41 cited
Arms Race in Adversarial Malware Detection: A Survey
Deqiang Li, Qianmu Li, Yanfang Ye +1
Malicious software (malware) is a major cyber threat that has to be tackled with Machine Learning (ML) techniques because millions of new malware examples are injected into cybersp…
cs.CR2020★ 60 cited
A Framework for Enhancing Deep Neural Networks Against Adversarial Malware
Deqiang Li, Qianmu Li, Yanfang Ye +1
Machine learning-based malware detection is known to be vulnerable to adversarial evasion attacks. The state-of-the-art is that there are no effective defenses against these attack…