Bozorgchenani, A. orcid.org/0000-0003-1360-6952, Zarakovitis, C.C. orcid.org/0000-0001-8729-0378, Chien, S.F. orcid.org/0000-0001-7637-9158 et al. (4 more authors) (2024) Intrusion Response Systems for the 5G Networks and Beyond: A New Joint Security-vs-QoS Optimization Approach. IEEE Transactions on Network Science and Engineering, 11 (3). 3039 -3052. ISSN 2327-4697
Abstract
Network connectivity exposes the network infrastructure and assets to vulnerabilities that attackers can exploit. Protecting network assets against attacks requires the application of security countermeasures. Nevertheless, employing countermeasures incurs costs, such as monetary costs, along with time and energy to prepare and deploy the countermeasures. Thus, an Intrusion Response System (IRS) shall consider security and QoS costs when dynamically selecting the countermeasures to address the detected attacks. This has motivated us to formulate a joint Security-vs-QoS optimization problem to select the best countermeasures in an IRS. The problem is then transformed into a matching game-theoretical model. Considering the monetary costs and attack coverage constraints, we first derive the theoretical upper bound for the problem and later propose stable matching-based solutions to address the trade-off. The performance of the proposed solution, considering different settings, is validated over a series of simulations.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Countermeasure selection, quality of service, optimization, matching game, intrusion response mechanisms |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 19 Feb 2024 10:54 |
Last Modified: | 23 May 2024 14:54 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Identification Number: | 10.1109/tnse.2024.3358170 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:209280 |