4 citations · 4 across the 9 of their papers we have counts for
7 papers · 1 filter
Malaika: Understanding Malware through Tri-Grounded Agentic Reasoning
Xingzhi Qian, Xinran Zheng, Yiling He +1
Recent LLM-based systems have shown promising capabilities for security-focused code analysis. Malware understanding, however, poses a distinct challenge: analysts must reconstruct…
Is "Knowing It's Malicious Enough?" Evaluating LLMs for Fine-Grained Malware Behavior Auditing
Xinran Zheng, Xingzhi Qian, Yiling He +2
Automated malware classifiers achieve strong detection performance, but auditing requires more than flagging a sample: analysts must explain malicious behaviors and justify them wi…
Large Language Models for Network Intrusion Detection Systems: Foundations, Implementations, and Future Directions
Shuo Yang, Xinran Zheng, Xinchen Zhang +5
Large Language Models (LLMs) have revolutionized various fields with their exceptional capabilities in understanding, processing, and generating human-like text. This paper investi…
Generative AI for Vulnerability Detection in 6G Wireless Networks: Advances, Case Study, and Future Directions
Shuo Yang, Xinran Zheng, Jinfeng Xu +4
The rapid advancement of 6G wireless networks, IoT, and edge computing has significantly expanded the cyberattack surface, necessitating more intelligent and adaptive vulnerability…
On Benchmarking Code LLMs for Android Malware Analysis
Yiling He, Hongyu She, Xingzhi Qian +4
Large Language Models (LLMs) have demonstrated strong capabilities in various code intelligence tasks. However, their effectiveness for Android malware analysis remains underexplor…
LAMD: Context-driven Android Malware Detection and Classification with LLMs
Xingzhi Qian, Xinran Zheng, Yiling He +2
The rapid growth of mobile applications has escalated Android malware threats. Although there are numerous detection methods, they often struggle with evolving attacks, dataset bia…
TIF: Learning Temporal Invariance in Android Malware Detectors
Xinran Zheng, Shuo Yang, Edith C. H. Ngai +2
Learning-based Android malware detectors degrade over time due to natural distribution drift caused by malware variants and new families. This paper systematically investigates the…