A Deep Transfer Learning-powered EDoS Detection Mechanism for 5G and Beyond Network Slicing
Benzaïd, Chafika; Taleb, Tarik; Sami, Ashkan; Hireche, Othmane (2024-02-26)
IEEE communications society
26.02.2024
C. Benzaïd, T. Taleb, A. Sami and O. Hireche, "A Deep Transfer Learning-Powered EDoS Detection Mechanism for 5G and Beyond Network Slicing," GLOBECOM 2023 - 2023 IEEE Global Communications Conference, Kuala Lumpur, Malaysia, 2023, pp. 4747-4753, doi: 10.1109/GLOBECOM54140.2023.10436891.
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© 2023 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.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202403052104
https://urn.fi/URN:NBN:fi:oulu-202403052104
Tiivistelmä
Abstract
Network slicing is recognized as a key enabler for 5G and beyond (B5G) services. However, its dynamic nature and the growing sophistication of DDoS attacks put it at risk of Economical Denial of Sustainability (EDoS) attack, causing economic losses to service provider due to the increased elastic use of resources. Motivated by the limitations of existing solutions, we propose FortisEDoS, a novel framework that aims at enabling EDoS-aware elastic B5G services. FortisEDoS integrates a new deep learning-based DDoS anomaly detection model, called CG-GRU, that leverages the capabilities of emerging graph and recurrent neural networks in capturing spatio-temporal correlations to accurately identify malicious behavior, allowing proactive mitigation of EDoS attacks. Moreover, FortisEDoS uses transfer learning to effectively counteract EDoS attacks in newly deployed slices by leveraging the knowledge acquired in previously deployed slice. The experimental results show the superiority of transfer learning-powered CG-GRU in achieving higher detection performance with lower computation overhead, compared to other baseline methods.
Network slicing is recognized as a key enabler for 5G and beyond (B5G) services. However, its dynamic nature and the growing sophistication of DDoS attacks put it at risk of Economical Denial of Sustainability (EDoS) attack, causing economic losses to service provider due to the increased elastic use of resources. Motivated by the limitations of existing solutions, we propose FortisEDoS, a novel framework that aims at enabling EDoS-aware elastic B5G services. FortisEDoS integrates a new deep learning-based DDoS anomaly detection model, called CG-GRU, that leverages the capabilities of emerging graph and recurrent neural networks in capturing spatio-temporal correlations to accurately identify malicious behavior, allowing proactive mitigation of EDoS attacks. Moreover, FortisEDoS uses transfer learning to effectively counteract EDoS attacks in newly deployed slices by leveraging the knowledge acquired in previously deployed slice. The experimental results show the superiority of transfer learning-powered CG-GRU in achieving higher detection performance with lower computation overhead, compared to other baseline methods.
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