Research on APT threat detection methods based on traceability graphs
Nowadays,global cybersecurity is facing serious challenges,especially the evolving Advanced Persistent Threats(APTs)seriously threaten global security.To address this problem,this paper proposes a new method based on traceability graph to effectively detect covert and persistent host-based threats without prior knowledge of the attack patterns.The method utilizes a graph neural network framework to learn the role of each entity in the original graph of data,which improves the sensitivity to covert threats,solves the problem of locating anomalous nodes,and enhances the ability to handle complex threats.
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