Year: 2025
Type: article
Abstract: n today's increasingly digital world, database security is a critical concern for government agencies handling classified information. This paper proposes a comprehensive security solution that integrates artificial intelligence (AI), machine learning (ML), and blockchain technologies to offer multiple layers of defense against sophisticated cyber threats. The system achieved 98.4% accuracy in detecting and mitigating attacks such as Denial of Service (DoS), User to Root (U2R), Remote to Local (R2L), and packet sniffing, utilizing K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), and convolutional neural network (CNN) classification algorithms. Blockchain technology was incorporated to ensure data integrity and transparency, enabling advanced threat detection and real-time response capabilities. The framework also features end-to-end access control, advanced audit systems, and strong incident-response protocols. The key objectives include investigating AI-driven user behavior, assessing AI versus traditional security methods, analyzing predictive capabilities, and developing integrated security recommendations. The proposed model offers a dynamic, adaptive, and future-proof solution for safeguarding sensitive government data from evolving cloud-based cyber threats.
Author: Christopher Mwololo Fred
Institution: Murang'a University of Technology
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4410215922