Botnet Vulnerability Intelligence Clustering Classification Mining and Countermeasure Algorithm Based on Machine Learning

Abstract

Botnet detection, vulnerability mining and confrontation bring many challenges to network security, and become the main dangerous source threatening economic development and causing significant economic losses. To solve these problems, we combine machine learning with classification mining, and propose a botnet vulnerability mining and countermeasure algorithm and its system. Firstly, a botnet model based on machine learning and its construction method are designed according to the characteristics of Botnet such as group network attack, computer group controlled and distributed denial of service attack. Then, in order to achieve the global optimal evaluation and screening of Botnet detection by network vulnerability mining system, a network vulnerability combination classification mining algorithm and machine learning instruction set are designed. Then, based on the analysis of the vulnerability form and the vulnerability diffusion mode of botnet, a vulnerability mining and confrontation algorithm for botnet is proposed. Finally, through simulation experiments, the advantages of the proposed algorithm are proved in vulnerability identification rate, vulnerability clearance performance and botnet connectivity. For example, the average vulnerability detection rate is more than 87%, the vulnerability repair rate is higher than 86%, and the pass rate is still higher than 89%.