Enhancing Database Security through AI-Based Intrusion Detection System

Abstract

Cybersecurity attacks on network database systems are becoming widespread, causing many problems for individuals and organizations. In order to improve access to the search system for database security, this study proposes the use of cognitive-based models. Artificial intelligence algorithms are used as the first step in determining the most important parts of network data. Database security is improved by using advanced technology. Four classification algorithms are used for intrusion detection: K nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), and a combination of neural network (CNN). The performance of the penetration testing model is demonstrated and analyzed using the test model and NSL-KDD datasets. According to the empirical results, the deployment method improves the access. The conclusion is that the proposed model is better than the original model. This study uses four classification algorithms to identify four types of network attacks, such as DoS attacks, U2R attacks, R2L attacks, and packet sniffing attacks, but the accuracy of the CNN classifier is higher than other classifications with 98.4% accuracy.