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Attack paths are the potential chain of malicious activities an attackerperforms to compromise network assets and acquire privileges through exploitingnetwork vulnerabilities. Attack path analysis helps organizations to identifynew/unknown chains of attack vectors that reach critical assets within thenetwork, as opposed to individual attack vectors in signature-based attackanalysis. Timely identification of attack paths enables proactive mitigation ofthreats. Nevertheless, manual analysis of complex network configurations,vulnerabilities, and security events to identify attack paths is rarelyfeasible. This work proposes a novel transferable graph neural network-basedmodel for shortest path identification. The proposed shortest path detectionapproach, integrated with a novel holistic and comprehensive model foridentifying potential network vulnerabilities interactions, is then utilized todetect network attack paths. Our framework automates the risk assessment ofattack paths indicating the propensity of the paths to enable the compromise ofhighly-critical assets (e.g., databases) given the network configuration,assets' criticality, and the severity of the vulnerabilities in-path to theasset. The proposed framework, named SPGNN-API, incorporates automated threatmitigation through a proactive timely tuning of the network firewall rules andzero-trust policies to break critical attack paths and bolster cyber defenses.Our evaluation process is twofold; evaluating the performance of the shortestpath identification and assessing the attack path detection accuracy. Ourresults show that SPGNN-API largely outperforms the baseline model for shortestpath identification with an average accuracy >= 95% and successfully detects100% of the potentially compromised assets, outperforming the attack graphbaseline by 47%.

SPGNN-API: A Transferable Graph Neural Network for Attack Paths Identification and Autonomous Mitigation