Big Data Intelligent Collection and Network Failure Analysis Based on Artificial Intelligence

Jun Ding, Roobaea Alroobaea, Abdullah M Baqasah, Anas Althobaiti, Rajan Miglani


In order to explore the intelligent collection of big data and network fault analysis, this paper proposes a big data intelligent collection and network fault analysis based on artificial intelligence. The construction of enterprise level information security situation awareness system is studied, and the model, architecture and specific implementation methods of information security are proposed. Through the design and deployment of the system, the effective detection, threat perception, risk determination and threat tracing of information security threats can be realized in enterprises, and the detection ability of enterprises to deal with security threats and security attacks can be comprehensively improved. The experimental results show that: Through this platform, the unknown threats analyzed by big data in the system can be manually intervened by professionals to make fine analysis, confirm attack means, target and purpose, and restore the full picture of the attacker through artificial intelligence combined with big data knowledge and multi-dimensional features of the attacker. Including homologous Trojan programs and malicious servers with different program forms, coding styles and attack principles, they "track" attackers through the overall features, continuously discover unknown threats, and ultimately ensure the accuracy of unknown threats discovery, and generate threat intelligence for the local analysis platform. It is proved that the intelligent acquisition of big data by artificial intelligence can effectively analyze network faults

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