Search: onr:"swepub:oai:DiVA.org:umu-199861" >
Reinforced Transfor...
Reinforced Transformer Learning for VSI-DDoS Detection in Edge Clouds
-
- Bhutto, Adil B. (author)
- Umeå universitet,Institutionen för datavetenskap
-
- Vu, Xuan-Son, 1988- (author)
- Umeå universitet,Institutionen för datavetenskap
-
- Elmroth, Erik (author)
- Umeå universitet,Institutionen för datavetenskap
-
show more...
-
- Tay, Wee Peng (author)
- School of Electrical & Electronics Engineering, Nanyang Technological University, Nanyang, Singapore
-
- Bhuyan, Monowar H. (author)
- Umeå universitet,Institutionen för datavetenskap
-
show less...
-
(creator_code:org_t)
- IEEE, 2022
- 2022
- English.
-
In: IEEE Access. - : IEEE. - 2169-3536. ; 10, s. 94677-94690
- Related links:
-
https://doi.org/10.1...
-
show more...
-
https://umu.diva-por... (primary) (Raw object)
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
show less...
Abstract
Subject headings
Close
- 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 factor 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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Cloud applications
- Cloud computing
- Computer crime
- Denial-of-service attack
- Edge clouds
- Image edge detection
- QoS/QoE
- Quality of service
- Reinforced transformer learning
- Reinforcement learning
- Throughput
- Transformers
- VSI-DDoS
Publication and Content Type
- ref (subject category)
- art (subject category)
Find in a library
To the university's database