Hybrid Algorithms for Real Time Anomaly Detection in Network Traffic

Abstract

Anomaly detection in network traffic is crucial for maintaining secure and reliable communication infrastructures. With the ever-increasing volume and complexity of data, as well as the continuous rise in sophisticated cyberattacks, real-time detection of anomalies has become a challenging task. Traditional detection techniques, which rely solely on either signature-based or anomaly-based approaches, frequently struggle to adapt to zero-day attacks, novel intrusions, or changing traffic patterns. In response, the research community has increasingly embraced hybrid algorithms that combine the strengths of multiple detection paradigms to achieve higher accuracy, lower false alarm rates, and faster detection times. This paper proposes a comprehensive framework for real-time anomaly detection in network traffic using hybrid algorithms that integrate supervised learning techniques with unsupervised methods. The approach leverages adaptive feature extraction, dynamic clustering, and classification to promptly identify anomalous patterns. Experimental results on publicly available benchmark datasets demonstrate improvements in detection accuracy and reduced latency in comparison to conventional approaches. A comparative analysis of different configurations within the hybrid framework is also provided. The outcomes highlight the potential of hybrid algorithms to transform network security by offering robust, real-time detection that outperforms single-method solutions.