Database intrusion risk data localization method based on improved ant colony algorithm
Yating Gao, Xingjie Huang, Jing Li, Jin Pang, Zixian Dong, Jing Lin Zhang · 2023
Database intruders usually take covert measures to hide their attacks and traces, so that the intrusion activities are not easy to be detected. This brings great challenge to database security management. Therefore, a database intrusion risk data location method based on improved ant colony algorithm is proposed. The optimization method of ant foraging path was improved, and the ant colony algorithm was optimized. Sample database intrusion based on improved ant colony algorithm. Using the sample object to mark the database intrusion behavior, based on this, the database intrusion risk data location algorithm is designed. To verify the effectiveness of the proposed method, an experiment is designed. Under the research method, the proportion of correct identification of database intrusion risk data and the proportion of real attack behavior in the targeted attack data can reach more than 99%. The length of the intrusion field located by the research method is consistent with the actual value. The experimental results show that the research method has ideal applicability.