Abstract
Attack graphs are a popular area of research that display all the paths an trespasser can take to penetrate a network. Existing graph generation methods rely heavily on expert opinion regarding network vulnerabilities and topology. An intrusion detection system based on graph-directed analytics of the big data analytics, which is based on machine learning, is proposed, which made it possible to obtain good accuracy results and a high level of attack detection during its testing. Keywords attack, graph, detection, machine learning, network
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