Machine Learning–Driven Behavioral Analysis of High-Volume Network Traffic for Advanced Persistent Threat Detection
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V5I2P109Keywords:
Advanced Persistent Threats (APT), Machine Learning, Network Traffic Analysis, Behavioral Modeling, Cybersecurity, Anomaly Detection, Intrusion Detection, High-Volume NetworksAbstract
The proliferation of high-volume network traffic in modern enterprises poses significant challenges for detecting Advanced Persistent Threats (APTs), which often evade traditional signature-based security mechanisms. This study presents a machine learning–driven framework for behavioral analysis of network traffic aimed at identifying APTs in real time. By leveraging both supervised and unsupervised learning models, the proposed approach constructs behavioral profiles of normal network activity and identifies deviations indicative of malicious actions. Extensive experiments were conducted on benchmark and simulated enterprise datasets, evaluating model performance in terms of detection accuracy, false positive rate, and computational efficiency. Results demonstrate that hybrid modeling, combining anomaly detection with pattern recognition, achieves superior detection of stealthy APT campaigns compared to conventional methods. Additionally, the framework addresses scalability and real-time deployment considerations, enabling its integration within high-throughput network environments. The findings highlight the potential of machine learning for proactive cybersecurity and provide actionable insights for enhancing enterprise threat monitoring systems. The study contributes a comprehensive methodology, experimental validation, and a reference architecture for ML-based behavioral analysis in high-volume networks
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