Reinforced transformer learning for VSI-DDoS detection in edge clouds
Edge-driven software applications often deployed as online services in the cloud-to-edge continuum lack significant protection for services and infrastructures against emerging cyberattacks. Very-Short Intermittent Distributed Denial of Service (VSI-DDoS) attack is one of the biggest factors for dim...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/164993 |
| _version_ | 1811688970905452544 |
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| author | Bhutto, Adil Bin Vu, Xuan Son Elmroth, Erik Tay, Wee Peng Bhuyan, Monowar |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Bhutto, Adil Bin Vu, Xuan Son Elmroth, Erik Tay, Wee Peng Bhuyan, Monowar |
| author_sort | Bhutto, Adil Bin |
| collection | NTU |
| description | Edge-driven software applications often deployed as online services in the cloud-to-edge continuum lack significant protection for services and infrastructures against emerging cyberattacks. Very-Short Intermittent Distributed Denial of Service (VSI-DDoS) attack is one of the biggest factors for diminishing the Quality of Services (QoS) and Quality of Experiences (QoE) for users on edge. Unlike conventional DDoS attacks, these attacks live for a very short time (on the order of a few milliseconds) in the traffic to deceive users with a legitimate service experience. To provide protection, we propose a novel and efficient approach for detecting VSI-DDoS attacks using reinforced transformer learning that mitigates the tail latency and service availability problems in edge clouds. In the presence of attacks, the users' demand for availing ultra-low latency and high throughput services deployed on the edge, can never be met. Moreover, these attacks send very-short intermittent requests towards the target services that enforce longer delays in users' responses. The assimilation of transformer with deep reinforcement learning accelerates detection performance under adverse conditions by adapting the dynamic and the most discernible patterns of attacks (e.g., multiplicative temporal dependency, attack dynamism). The extensive experiments with testbed and benchmark datasets demonstrate that the proposed approach is suitable, effective, and efficient for detecting VSI-DDoS attacks in edge clouds. The results outperform state-of-the-art methods with 0.9%-3.2% higher accuracy in both datasets. |
| first_indexed | 2024-10-01T05:40:40Z |
| format | Journal Article |
| id | ntu-10356/164993 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:40:40Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1649932023-03-10T15:40:05Z Reinforced transformer learning for VSI-DDoS detection in edge clouds Bhutto, Adil Bin Vu, Xuan Son Elmroth, Erik Tay, Wee Peng Bhuyan, Monowar School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Reinforced Transformer Learning Edge Clouds Edge-driven software applications often deployed as online services in the cloud-to-edge continuum lack significant protection for services and infrastructures against emerging cyberattacks. Very-Short Intermittent Distributed Denial of Service (VSI-DDoS) attack is one of the biggest factors for diminishing the Quality of Services (QoS) and Quality of Experiences (QoE) for users on edge. Unlike conventional DDoS attacks, these attacks live for a very short time (on the order of a few milliseconds) in the traffic to deceive users with a legitimate service experience. To provide protection, we propose a novel and efficient approach for detecting VSI-DDoS attacks using reinforced transformer learning that mitigates the tail latency and service availability problems in edge clouds. In the presence of attacks, the users' demand for availing ultra-low latency and high throughput services deployed on the edge, can never be met. Moreover, these attacks send very-short intermittent requests towards the target services that enforce longer delays in users' responses. The assimilation of transformer with deep reinforcement learning accelerates detection performance under adverse conditions by adapting the dynamic and the most discernible patterns of attacks (e.g., multiplicative temporal dependency, attack dynamism). The extensive experiments with testbed and benchmark datasets demonstrate that the proposed approach is suitable, effective, and efficient for detecting VSI-DDoS attacks in edge clouds. The results outperform state-of-the-art methods with 0.9%-3.2% higher accuracy in both datasets. Published version This work was supported in part by the STINT Project funded by the Swedish Foundation for International Cooperation in Research and Higher Education; and in part by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by Knut and Alice Wallenberg Foundation. 2023-03-07T02:27:27Z 2023-03-07T02:27:27Z 2022 Journal Article Bhutto, A. B., Vu, X. S., Elmroth, E., Tay, W. P. & Bhuyan, M. (2022). Reinforced transformer learning for VSI-DDoS detection in edge clouds. IEEE Access, 10, 94677-94690. https://dx.doi.org/10.1109/ACCESS.2022.3204812 2169-3536 https://hdl.handle.net/10356/164993 10.1109/ACCESS.2022.3204812 2-s2.0-85137853452 10 94677 94690 en IEEE Access © 2022 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. application/pdf |
| spellingShingle | Engineering::Electrical and electronic engineering Reinforced Transformer Learning Edge Clouds Bhutto, Adil Bin Vu, Xuan Son Elmroth, Erik Tay, Wee Peng Bhuyan, Monowar Reinforced transformer learning for VSI-DDoS detection in edge clouds |
| title | Reinforced transformer learning for VSI-DDoS detection in edge clouds |
| title_full | Reinforced transformer learning for VSI-DDoS detection in edge clouds |
| title_fullStr | Reinforced transformer learning for VSI-DDoS detection in edge clouds |
| title_full_unstemmed | Reinforced transformer learning for VSI-DDoS detection in edge clouds |
| title_short | Reinforced transformer learning for VSI-DDoS detection in edge clouds |
| title_sort | reinforced transformer learning for vsi ddos detection in edge clouds |
| topic | Engineering::Electrical and electronic engineering Reinforced Transformer Learning Edge Clouds |
| url | https://hdl.handle.net/10356/164993 |
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