2771-1463ISSN (Online)
2771-1471ISSN (Print)
Volume 10 , Issue 1 , PP: 14-22, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
C. Meenaloshini 1 * , A. R. Darshika Kelin 2 , Keirolona Safana Seles 3
Doi: https://doi.org/10.54216/JCHCI.100102
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.
Cyber threat detection , Network traffic analysis , Real-time detection , Machine learning , Anomaly detection , Adaptive systems , Intrusion detection systems , Supervised learning , Unsupervised learning
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