1. Combating Concept Drift with Explanatory Detection and Adaptation for Android Malware Classification (2025 ACM SIGSAC Conference on Computer and Communications Security)
2. On Benchmarking Code LLMs for Android Malware Analysis (2025 ACM SIGSOFT International Symposium on Software Testing and Analysis)
3. LAMD: Context-driven Android Malware Detection and Classification with LLMs (2025 IEEE Security and Privacy Workshops, Best Paper Award)
4. Explanation as a Watermark: Towards Harmless and Multi-bit Model Ownership Verification via Watermarking Feature Attribution (Network and Distributed System Security Symposium)
5. Pitfalls in Language Models for Code Intelligence: A Taxonomy and Survey
Research Experience
Currently working as a Research Fellow at University College London.
Background
Research interests include code LLM applications, focusing on the problems of semantics preserving and robustness to drift. Continuously learning recent advances in related domains and never limiting to one way. Looking to collaborate on LLM-powered software security, e.g., for vulnerability detection, malware summary, and defense.