Network security risk assessment of football matches based on data mining technology

Abstract

ObjectiveUsing the principles and advanced methods of data mining technology, this paper aims to achieve the analysis and assessment of network security risks under soccer tournaments as a way to help analyze and solve the objective problems such as system vulnerabilities and network viruses that exist in network security today. Faced with various network securities, this paper adopts quantitative risk assessment method. MethodsIn the evaluation of algorithms for constructing network security, the principles and laws of data mining techniques are drawn upon for the analysis and evaluation of network security risks to help analyze the network security risks of soccer tournaments. ResultIt contains 494,020 network connection data, each of which contains 41 connection attributes, including 34 numeric and 7 character attributes. There were 97,277 normal network connections and 396,743 anomalous network connections. In the experiments of the anomaly detection module, although the false positive rate increases compared to all features, the accuracy is basically the same as that of all features with nearly 50% less modeling time and lower false positive rate. So this method is more stable. Therefore, the analysis and assessment of cybersecurity risks are crucial in the present time.