A survey of streaming data anomaly detection in network security.

Abstract

Cybersecurity has always been a subject of great concern, and anomaly detection has gained increasing attention due to its ability to detect novel attacks. However, network anomaly detection faces significant challenges when dealing with massive traffic, logs, and other forms of streaming data. This article provides a comprehensive review and a multi-faceted analysis of recent algorithms for anomaly detection in network security. It systematically categorizes and elucidates the various types of datasets, measurement techniques, detection algorithms, and output results of streaming data. Furthermore, the review critically compares network security application scenarios and problem-solving capabilities of streaming data anomaly detection methods. Building on this analysis, the study identifies and delineates promising future research directions. This article endeavors to achieve rapid and efficient detection of streaming data, thereby providing better security for network operations. This research is highly significant in addressing the challenges and difficulties of analyzing anomalies in streaming data. It also serves as a valuable reference for further development in the field of network security. It is anticipated that this comprehensive review will serve as a valuable resource for security researchers in their future investigations within network security.