Attack prediction method based on knowledge graph and reinforcement learning
To address the problems of poor applicability,low knowledge utilization and difficulty in dealing with diverse attack threats for current attack prediction methods,an attack prediction method based on knowledge graph and reinforcement learning was proposed.Firstly,a cyber security knowledge graph and an attack scenario knowledge graph were constructed.Secondly,the knowledge representation learning and deep reinforcement learning methods were integrated to propose an attack prediction knowl-edge reasoning model RLBTransE.Based on the attack scenario network topology and attack scenario knowledge graph,inter-host attack paths and single-host attack paths were generated respectively,and finally the complete attack path prediction was realized.Experimental results on the simulated experimental scenario data set show that,compared with current typical advanced methods,RLBTransE improves the mean reciprocal rank(MRR)and Hits@1 by 10.1%and 9.3%,respectively.Comparative experiments with other attack prediction methods also verify the better applicability and interpretability of this method.
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