14 citations · 35 across the 7 of their papers we have counts for
7 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…
An Empirical Evaluation of GDPR Compliance Violations in Android mHealth Apps
Ming Fan, Le Yu, Sen Chen +6
The purpose of the General Data Protection Regulation (GDPR) is to provide improved privacy protection. If an app controls personal data from users, it needs to be compliant with G…
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…
A Survey of Smart Contract Formal Specification and Verification
Palina Tolmach, Yi Li, Shang-Wei Lin +2
A smart contract is a computer program which allows users to automate their actions on the blockchain platform. Given the significance of smart contracts in supporting important ac…
CoreGen: Contextualized Code Representation Learning for Commit Message Generation
Lun Yiu Nie, Cuiyun Gao, Zhicong Zhong +3
Automatic generation of high-quality commit messages for code commits can substantially facilitate software developers' works and coordination. However, the semantic gap between so…
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…