Enhancing Database Security through AI-Based Intrusion Detection System

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ID: 312940
2024
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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.
Reference Key
imported_1777057948_69ebc09c98585 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Muhammad Sohail Afzal
Journal Journal of Computing & Biomedical Informatics
Year 2024
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/563
Keywords Keywords not found

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