Intrusion Detection Systems (IDS) and anomaly detection techniques underpin modern cybersecurity by autonomously monitoring network activities and flagging deviations from normal behaviour. IDS are ...

Network anomaly detection is an important and dynamic research area. Many Network Intrusion Detection methods and Systems (NIDS) have been proposed in the literature. In this paper, the authors ...

Violations of security policies within a computer or network are symbolic of the need for robust intrusion detection. From attackers accessing systems from the internet or authorized users conducting ...

Are you aware that your API gateway, a vital component of modern software architecture, is also one of the most vulnerable points in a network? Shockingly, a 2022 survey by Statista revealed that most ...

In the insideAI News Research Highlights column we take a look at new and upcoming results from the research community for data science, machine learning, AI and deep learning. Our readers need to get ...

Phil Goldstein is a former web editor of the CDW family of tech magazines and a veteran technology journalist. He lives in Washington, D.C., with his wife and their animals: a dog named Brenna, and ...

(1) An approach to intrusion detection that establishes a baseline model of behavior for users and components in a computer system or network. Deviations from the baseline cause alerts that direct the ...

Explore how AI-driven anomaly detection enhances the security of Model Context Protocol (MCP) deployments, protecting AI infrastructure from evolving threats with real-time insights.

As the volume of cyberattacks grows, security analysts have become overwhelmed. To address this issue, developers are showing more interest in using Machine Learning (ML) to automate threat-hunting.

Intrusion detection systems (IDS) and anomaly detection techniques are critical components of modern cybersecurity, enabling the identification of malicious activities and system irregularities in ...