Enhancing Cyber Threat Detection through Machine Learning-Based Behavioral Modeling of Network Traffic Patterns

Abstract

Cyber threats and data breaches have become more sophisticated and stealthier over time. Traditional rule-based intrusion detection systems fail to detect many modern attacks. This paper explores how machine learning can enhance cyber threat detection through behavioral modeling of network traffic patterns. Anomaly detection based on machine learning provides adaptive protection by learning normal behavior and identifying deviations that may indicate malicious activity. We present an overview of key machine learning techniques, explain how they model complex patterns in network traffic data not discernible by rules, assess challenges in practical application, and provide guidance for maximizing detection capability. Our methodology utilizes supervised, unsupervised, and hybrid machine learning algorithms, including neural networks, support vector machines, random forests, self-organizing maps, k-means clustering, and isolation forests. We evaluate performance based on detection rate, false positive rate, accuracy, precision, recall, and f1-score. Results demonstrate that machine learning significantly improves detection rates over conventional techniques, while maintaining manageable false positives. We conclude with recommendations for production deployment, monitoring for concept drift, and future research directions. The behavioral modeling approach outlined modernizes cyber threat detection to meet the demands of today's dynamic threat landscape.