Enhanced Real-Time Detection of Cyber Threats through Adaptive Machine Learning in Network Traffic Analysis

C. Meenaloshini^1,*, A. R. Darshika Kelin¹, Keirolona Safana Seles²

¹Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore, India

²Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore, India

Emails: meenaloshinic@karunya.edu.in; darshika@karunya.edu; keirolonasafana@karunya.edu

Abstract

As cyber threats become more complex, real-time systems are needed to detect and eliminate attacks. Traditional network intrusion detection systems based on rule based static method tend to be ineffective against novel emerging threats. In this paper, we propose an improved real time cyber threat detection system using adaptive machine learning techniques used to analyze network traffic and find anomalies. Our proposed approach uses a blend of supervised and unsupervised learning models such that the system maintains high detection accuracy with minimal false positives, while maintaining continuous adaptation to constantly evolving threats. On critical network traffic features like packet size, flow duration, source and destination IP addresses, transmission protocols, the system is then trained. They show experimentally better detection accuracy, responsiveness and adaptability than conventional IDS. In this work, contributions of adaptive machine learning for robustness against dynamic and evolving threats in network environments are highlighted as significant strides towards improving real time cybersecurity infrastructure.

Keywords: Cyber threat detection; Network traffic analysis; Real-time detection; Machine learning; Anomaly detection; Adaptive systems; Intrusion detection systems; Supervised learning; Unsupervised learning