As cyber-attacks and network vulnerabilities get more sophisticated, optimizing protection systems utilizing advanced computational methodologies is critical. Big data approaches are a viable way to simulate network attacks and improve protection measures. But conventional approaches frequently can’t handle massive volumes of real-time data or quickly adjust to changing threats. The goal of the research is to create a large data-driven computer-aided network attack simulation and defense system that uses advanced Machine Learning (ML) techniques to optimize defensive techniques, improve threat detection, and increase system adaptability. A hybrid system that combines big data analytics with Hyperbolic Tangent Particle Swarm Optimized Decision Tree (HTPSO-DT) methods was presented to simulate and predict possible cyber-attacks. Big data refers to the methodologies for collecting, processing by min–max scaling, and extracting insights from diverse, high-volume, and high-velocity data sets using Discrete Wavelet Transform (DWT). The system simulates attack scenarios and optimizes defense responses using real-time network traffic data; behavior analysis and predictive modeling. The defense system adjusts by continuously learning from simulations and continually improving its techniques. The proposed method of HTPSO-DT has performed and achieved precision at 99.12%, recall at 99.15%, [Formula: see text]1 score at 99.17%, [Formula: see text]2 score at 99.09%, [Formula: see text]beta score at 98.95%, and ROC-AUU at 0.88. The method significantly improves attack detection accuracy and reduces defense response time. The suggested solution improves the effectiveness of network defense strategies by integrating big data and ML, allowing for real-time, adaptive protection.
原文链接
分享
引用
