60 citations · 151 across the 5 of their papers we have counts for
5 papers
Can We Leverage Predictive Uncertainty to Detect Dataset Shift and Adversarial Examples in Android Malware Detection?
Deqiang Li, Tian Qiu, Shuo Chen +2
The deep learning approach to detecting malicious software (malware) is promising but has yet to tackle the problem of dataset shift, namely that the joint distribution of examples…
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…
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…
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…
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…