5 papers
From Signals to Behaviors: Evidence-Based Android Malware Detection
Shiwen Song, Yiheng Xiong, Sen Chen +1
Android malware remains a persistent threat, and detecting it accurately is a long-standing open problem. Whether an app is malicious depends on what it actually does and the conte…
From Exploration to Specification: LLM-Based Property Generation for Mobile App Testing
Yiheng Xiong, Shiwen Song, Bo Ma +2
Mobile apps often suffer from functional bugs that do not cause crashes but instead manifest as incorrect behaviors under specific user interactions. Such bugs are difficult to det…
How Effective Are NPM Malicious Package Detectors? A Large-Scale Empirical Study
Wenbo Guo, Zhongwen Chen, Zhengzi Xu +7
The NPM ecosystem faces escalating threats from malicious packages that exploit its open publication model. While numerous detection tools have been proposed, they are evaluated on…
Bridging Expert Reasoning and LLM Detection: A Knowledge-Driven Framework for Malicious Packages
Wenbo Guo, Shiwen Song, Jiaxun Guo +5
Open-source ecosystems such as NPM and PyPI are increasingly targeted by supply chain attacks, yet existing detection methods either depend on fragile handcrafted rules or data-dri…
FCGHunter: Towards Evaluating Robustness of Graph-Based Android Malware Detection
Shiwen Song, Xiaofei Xie, Ruitao Feng +2
Graph-based detection methods leveraging Function Call Graphs (FCGs) have shown promise for Android malware detection (AMD) due to their semantic insights. However, the deployment…