6 papers
T2S: A Rehearsal-Based Approach for Extraction-Resistant Model Watermarking
Jian-Ping Mei, Weibin Zhang, Ao Yao +2
Model watermarking safeguards AI model intellectual property by embedding distinctive knowledge that induces unique behavioral signatures. The primary technical challenge lies in e…
ZERO-APT: A Closed-Loop Adversarial Framework for LLM-Driven Automated Penetration Testing under Intelligent Defense
Anlan Zheng, Tiantian Zhu
LLM-driven automated penetration testing agents are typically evaluated against static targets that neither detect nor respond to attacks, so their behavior under intelligent defen…
ProHunter: A Comprehensive APT Hunting System Based on Whole-System Provenance
Xuebo Qiu, Mingqi Lv, Yimei Zhang +2
Advanced Persistent Threats (APTs) remain difficult to detect due to their stealthy nature and long-term persistence. To tackle this challenge, provenance-based threat hunting has…
APT-MCL: An Adaptive APT Detection System Based on Multi-View Collaborative Provenance Graph Learning
Mingqi Lv, Shanshan Zhang, Haiwen Liu +2
Advanced persistent threats (APTs) are stealthy and multi-stage, making single-point defenses (e.g., malware- or traffic-based detectors) ill-suited to capture long-range and cross…
APT-CGLP: Advanced Persistent Threat Hunting via Contrastive Graph-Language Pre-Training
Xuebo Qiu, Mingqi Lv, Yimei Zhang +4
Provenance-based threat hunting identifies Advanced Persistent Threats (APTs) on endpoints by correlating attack patterns described in Cyber Threat Intelligence (CTI) with provenan…
D-ADD: An Effective Plug-In for Defending Against Model Stealing
Jian-Ping Mei, Weibin Zhang, Jie Chen +2
Malicious users attempt to replicate commercial models functionally at low cost by training a clone model with query responses. Timely prevention of such model-stealing attacks is…