TL;DR
A novel algorithm, Arbitrary Number Producer with Categorized Intrusion Detection System (ANP-CIDS), designed to address 5G challenges by effectively countering two critical types of cyber-attacks: sleep deprivation attacks and Sybil attacks is introduced.
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.
As interconnected devices increasingly transmit personal and sensitive data, security attacks are becoming more sophisticated and prevalent, highlighting the critical need for effective security solutions in Internet of Things (IoT) environments. An automated Network Intrusion Detection (NID) system plays a vital role...
Evidence from recent surveys, standards and industry reports indicates that AI can shorten detection and response times and reduce breach costs, but only when it is governed carefully and protected as a target in its own right.
A privacy-friendly intrusion detection system (PP-IDS) that operates on the basis of conjoint learning, which makes PP-IDS suitable for cloud services, edge devices, Internet of Things networks or business systems – basically anywhere that data needs to remain private.
This paper analyzes eight major IT/OT threats in the view of their empowerment via steganography to anticipate the evolution of malicious software targeting IT/OT scenarios when endowed with advanced data hiding schemes, i.e., multi level steganography.
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.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
