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Intelligent Penetration Testing Through Integrated Knowledge Graph and Historical Decision Enhancement
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DOI：10.1109/tdsc.2026.3694384.png)
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Abstract
En 中文
Penetration Testing (PT), a key network security assessment technique that simulates real cyber attacks to identify vulnerabilities, is traditionally manual and expert-dependent, leading to low efficiency and high costs. Automating and intelligentizing PT has thus become a critical research focus, yet current technologies face two core challenges: lack of standardized, reusable simulated network scenarios (hindering unified experiments and result comparison) and intelligent models’ failure to integrate historical decision experience or utilize attack chain temporal correlations (restricting adaptability). To address these, this study proposes an intelligent PT method integrating knowledge graph-driven automated scenario construction and historical decision enhancement. Two innovations are introduced: a network knowledge graph-based mechanism to generate standardized, real-characteristic testing environments; and a historical decision enhancement scheme with a collaborative state temporal processing and action filtering architecture. Experimental results show the method reduces average iterations by 69%, eliminates redundant executions, and enhances decision rationality, offering a new path for automated PT advancement.
Keywords:
Penetration testing
reinforcement learning
network scenario construction
historical decision enhancement
network security
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