Cyber threats and data breaches have become more sophisticated and stealthier over time.
Traditional rule-based intrusion detection systems fail to detect many modern attacks. This …

With the rapid rise in the ubiquity and sophistication of Internet technology and the
accompanying growth in the number of network attacks, network intrusion detection has …

Much of the intrusion detection research focuses on signature (misuse) detection, where
models are built to recognize known attacks. However, signature detection, by its nature …

Аннотация This article delves into the application of machine learning techniques in
network security for proactive threat detection. It explores how artificial intelligence …

Rapid developments in network technologies and the amount and scope of data transferred
on networks are increasing day by day. Depending on this situation, the density and …

Intrusions in computer networks have increased significantly in the last decade, due in part
to a profitable underground cyber-crime economy and the availability of sophisticated tools …

This study summarizes major literature reviews on machine learning systems for network
analysis and intrusion detection. Furthermore, it provides a brief lesson description of each …

This paper discusses the application and advantages of machine learning in anomaly
detection of network security traffic. By summarizing the existing methods and techniques of …

This research introduces a theoretical framework for network anomaly detection in
cybersecurity, emphasizing the integration of adaptive machine learning models, ensemble …

Machine learning has been leveraged to increase the effectiveness of intrusion detection
systems (IDSs). The focus of this approach, however, has largely be on detecting known …