3 papers
cs.CR2026
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…
cs.CR2025
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…
cs.LG2022
Excitement Surfeited Turns to Errors: Deep Learning Testing Framework Based on Excitable Neurons
Haibo Jin, Ruoxi Chen, Haibin Zheng +4
Despite impressive capabilities and outstanding performance, deep neural networks (DNNs) have captured increasing public concern about their security problems, due to their frequen…