8 citations · 25 across the 6 of their papers we have counts for
6 papers
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…
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…
The HitchHiker's Guide to High-Assurance System Observability Protection with Efficient Permission Switches
Chuqi Zhang, Jun Zeng, Yiming Zhang +4
Protecting system observability records (logs) from compromised OSs has gained significant traction in recent times, with several note-worthy approaches proposed. Unfortunately, no…
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…
Automatic Root Cause Analysis via Large Language Models for Cloud Incidents
Yinfang Chen, Huaibing Xie, Minghua Ma +15
Ensuring the reliability and availability of cloud services necessitates efficient root cause analysis (RCA) for cloud incidents. Traditional RCA methods, which rely on manual inve…
AttacKG: Constructing Technique Knowledge Graph from Cyber Threat Intelligence Reports
Zhenyuan Li, Jun Zeng, Yan Chen +1
Cyber attacks are becoming more sophisticated and diverse, making detection increasingly challenging. To combat these attacks, security practitioners actively summarize and exchang…