A Machine Learning-Based Framework for Intelligent Cybersecurity Threat Detection and Prevention

Abstract: The increasing sophistication of cyberattacks poses critical challenges to modern network infrastructure security. Traditional signature-based intrusion detection systems (IDS) are becoming obsolete against zero-day exploits, polymorphic malware, and advanced persistent threats (APTs). This paper presents a comprehensive Machine Learning (ML)-based framework for intelligent cybersecurity threat detection and prevention capable of identifying eight distinct attack categories including Denial-of-Service (DoS), Probe, Remote-to-Local (R2L), User-to-Root (U2R), Botnet, Brute Force, SQL Injection, and Cross-Site Scripting (XSS). We design and evaluate a multi-tier classification pipeline comprising Random Forest (RF), Gradient Boosting (GB), and Logistic Regression (LR) algorithms on a synthetic, NSL-KDD-inspired dataset of 4,506 labeled network traffic samples with 35 engineered features. Our framework achieves 100% accuracy in binary threat classification (normal vs. attack) and 90.91% accuracy in multiclass attack-type classification, with a weighted F1-score of 0.9114. Feature importance analysis reveals that behavioral indicators such as num_compromised, src_bytes, num_file_creations, and packet anomaly flags are the strongest predictors of malicious activity. The proposed framework further incorporates a real-time prevention module with adaptive firewall rules, automated incident response, and feedback-driven model retraining. Comparative analysis demonstrates statistically significant improvements over traditional SVM and neural network baselines. This research provides a scalable, explainable, and deployable ML security pipeline suitable for enterprise-grade network environments.

Keywords: Cybersecurity, Intrusion Detection System, Machine Learning, Random Forest, Network Traffic Classification, Threat Prevention, Deep Learning, Anomaly Detection, Feature Engineering, Zero-Day Attacks