3 papers
cs.CR2020
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…
cs.CR2018
Enhancing Robustness of Deep Neural Networks Against Adversarial Malware Samples: Principles, Framework, and AICS'2019 Challenge
Deqiang Li, Qianmu Li, Yanfang Ye +1
Malware continues to be a major cyber threat, despite the tremendous effort that has been made to combat them. The number of malware in the wild steadily increases over time, meani…
cs.CR2018
HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples
Deqiang Li, Ramesh Baral, Tao Li +3
Adversarial machine learning in the context of image processing and related applications has received a large amount of attention. However, adversarial machine learning, especially…