activity
20172020
most citedAn Empirical Evaluation of GDPR Compliance Violations in Android mHealth Apps

14 citations · 30 across the 4 of their papers we have counts for

collaborators

8 papers

cs.SE202014 cited

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…

cs.CR20205 cited

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…

cs.CR2020

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…

cs.SE2020

Automated synthesis of local time requirement for service composition

Étienne André, Tian Huat Tan, Manman Chen +4

Service composition aims at achieving a business goal by composing existing service-based applications or components. The response time of a service is crucial especially in time c…

cs.LG2019

Machine Learning Testing: Survey, Landscapes and Horizons

Jie M. Zhang, Mark Harman, Lei Ma +1

This paper provides a comprehensive survey of Machine Learning Testing (ML testing) research. It covers 144 papers on testing properties (e.g., correctness, robustness, and fairnes…

cs.CR20198 cited

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…