Joint Security-vs-QoS Framework : Optimizing the Selection of Intrusion Detection Mechanisms in 5G networks

Bozorgchenani, Arash and Zarakovitis, Charilaos and Fong Chien, Su and Siong Lim, Heng and Ni, Qiang and Gouglidis, Antonios and Mallouli, Wissam (2022) Joint Security-vs-QoS Framework : Optimizing the Selection of Intrusion Detection Mechanisms in 5G networks. In: ARES '22: Proceedings of the 17th International Conference on Availability, Reliability and Security :. ACM, New York. ISBN 9781450396707

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Abstract

The advent of 5G technology introduces new - and potentially undiscovered - cybersecurity challenges, with unforeseen impacts on our economy, society, and environment. Interestingly, Intrusion Detection Mechanisms (IDMs) can provide the necessary network monitoring to ensure - to a big extent - the detection of 5G-related cyberattacks. Yet, how to realize the attack surface of 5G networks with respect to the detected risks, and, consequently, how to optimize the cybersecurity levels of the network, remains an open critical challenge. In respect, this work focuses on deploying multiple distributed Security Agents (SAs) that can run different IDMs over various network components and proposes a cybersecurity mechanism for optimizing the network’s attack surface with respect to the Quality of Service (QoS). The proposed approach relies on a new closed-form utility function to describe the trade-off between cybersecurity and QoS and uses multi-objective optimization to improve the selection of each SA detection level. We demonstrate via simulations that before optimization, an increase in the detection level of SAs brings a direct decrease in QoS as more computational, bandwidth and monetary resources are utilized for IDM processing. Thereby, after optimization, we demonstrate that our mechanism can strike a balance between cybersecurity and QoS while showcasing the impact of the importance of different objectives of the joint optimization.