An Advance Machine Learning (ML) Approaches for Anomaly Detection based on Network Traffic

Abstract

With the growth in the number of network users because of improvements in network technologies, there has been a corresponding increase in the traffic data on networks, which has become susceptible to attacks and intrusions. Hence, there is a growing need for effective security mechanisms for anomaly detection and prevention of intrusions. Of late, network security has drawn much attention from researchers and labs. This paper discusses the present review of current progressions of anomaly detection based on published studies in the past five years. Modern techniques and opportunities are considered along with discussing various applications of such technologies as WSNs, the Internet of Things (IoT), high-performance computing, industrial control systems, and SDNs. Advancement in machine learning and artificial intelligence has improved techniques for anomaly detection. Supervised, unsupervised, and semi-supervised learning improve the detectability of attacks. Supervised models make use of labeled datasets to recognize known attack patterns, while unsupervised models identify new ones by analyzing traffic behavior without prior threat knowledge. Hybrid approaches that combine multiple approaches are becoming a robust solution to the complexities of network traffic. The paper further introduces ongoing challenges in enhancing the anomaly detection system with regard to the handling and reduction of false positives in high-dimensional data and achieving real-time processing for increased reliability. All this detailed analysis is done to make an attempt at a clearer picture of the present scenario with regard to anomaly detection and the upcoming trends in network security.

Keywords-: Anomaly Detection, Networks, Supervised, Intrusion, Unsupervised, Anomaly Detection