Authors: Priya Narayanan
Abstract: Machine learning has emerged as a powerful approach for detecting anomalies in modern network environments, where traditional rule-based security systems often fail to identify evolving and sophisticated cyber threats. With the exponential growth of network traffic and the increasing complexity of distributed systems, ensuring real-time threat detection has become a critical requirement. This study explores the application of machine learning techniques for anomaly detection in network systems, focusing on supervised, unsupervised, and semi-supervised learning methods. These techniques enable the identification of unusual patterns in network traffic that may indicate intrusions, malware activity, or unauthorized access. The paper also examines the integration of machine learning models with network monitoring tools, intrusion detection systems, and cloud-based security platforms. Furthermore, it discusses key challenges such as high false-positive rates, data imbalance, concept drift, and scalability issues. Emerging solutions including deep learning models, autoencoders, and real-time streaming analytics are also highlighted. The findings indicate that machine learning significantly enhances the accuracy, adaptability, and efficiency of network anomaly detection systems, making them essential for modern cybersecurity frameworks.
DOI: http://doi.org/
