3 papers
cs.CR2026
Heimdallr: Characterizing and Detecting LLM-Induced Security Risks in GitHub CI Workflows
Bonan Ruan, Yeqi Fu, Chuqi Zhang +3
GitHub Continuous Integration (CI) workflows increasingly integrate Large Language Models (LLMs) to automate review, triage, content generation, and repository maintenance. This cr…
cs.CR2026
Unraveling the Key of Machine Learning-based Android Malware Detection
Jiahao Liu, Jun Zeng, Fabio Pierazzi +3
With the rapid advancement of machine learning (ML), ML-based Android malware detection has gained significant popularity due to its ability to automatically learn malicious patter…
cs.CR2024
MASKDROID: Robust Android Malware Detection with Masked Graph Representations
Jingnan Zheng, Jiaohao Liu, An Zhang +4
Android malware attacks have posed a severe threat to mobile users, necessitating a significant demand for the automated detection system. Among the various tools employed in malwa…