TL;DR
This work presents an intrusion detection framework based on a bio-inspired learning strategy derived from the grouping cockroach classifier (GCC) that achieves a classification accuracy of 99.21% while maintaining a low false alarm rate, outperforming several conventional machine learning methods.
The combination of increasing connectivity to the outside world and the lack of inbuilt security mechanisms has made legacy intra-vehicular networks extremely vulnerable to cyberattacks. Consequently, significant research has been devoted to analysing the impact of potential attacks, yielding datasets crucial for devel...
This study provides a high-precision, lightweight, and strongly generalizable multi-class intrusion detection model for resource-constrained vehicular environments.
The rapid development of the Internet of Vehicles (IoV) has significantly increased the exposure of intelligent transportation systems to sophisticated cyber threats, particularly targeting in-vehicle communication networks such as the Controller Area Network (CAN). Conventional intrusion detection systems and traditio...
An advanced AI-powered, real-time defense against increasingly sophisticated cyber-physical threats is presented, designed for real-time detection and mitigation of CPAs in modern grids.
This review provides a detailed study of intrusion detection systems in 5G networks that are federated learning-based, and how the federated learning-based intrusion detection systems can address the challenges mentioned above.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
