14 citations · 31 across the 6 of their papers we have counts for
6 papers · 1 filter
Risk Management Framework for Machine Learning Security
Jakub Breier, Adrian Baldwin, Helen Balinsky +1
Adversarial attacks for machine learning models have become a highly studied topic both in academia and industry. These attacks, along with traditional security threats, can compro…
Can We Trust Your Explanations? Sanity Checks for Interpreters in Android Malware Analysis
Ming Fan, Wenying Wei, Xiaofei Xie +3
With the rapid growth of Android malware, many machine learning-based malware analysis approaches are proposed to mitigate the severe phenomenon. However, such classifiers are opaq…
Why an Android App is Classified as Malware? Towards Malware Classification Interpretation
Bozhi Wu, Sen Chen, Cuiyun Gao +4
Machine learning (ML) based approach is considered as one of the most promising techniques for Android malware detection and has achieved high accuracy by leveraging commonly-used…
Superion: Grammar-Aware Greybox Fuzzing
Junjie Wang, Bihuan Chen, Lei Wei +1
In recent years, coverage-based greybox fuzzing has proven itself to be one of the most effective techniques for finding security bugs in practice. Particularly, American Fuzzy Lop…
DeepLaser: Practical Fault Attack on Deep Neural Networks
Jakub Breier, Xiaolu Hou, Dirmanto Jap +3
As deep learning systems are widely adopted in safety- and security-critical applications, such as autonomous vehicles, banking systems, etc., malicious faults and attacks become a…
An Empirical Assessment of Security Risks of Global Android Banking Apps
Sen Chen, Lingling Fan, Guozhu Meng +5
Mobile banking apps, belonging to the most security-critical app category, render massive and dynamic transactions susceptible to security risks. Given huge potential financial los…