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RTVD: A Real-Time Volumetric Detection Scheme for DDoS in the Internet of Things
- Source :
- IEEE Access, Vol 8, Pp 36191-36201 (2020)
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- © 2013 IEEE. Distributed Denial of Service (DDoS) attacks are increasingly harmful to the cyberspace nowadays. The attackers can now easily launch a bigger and more challenging DDoS attack both towards and with Internet-of-Things (IoT) devices, due to the fast popularization of them. Because of the characteristic of fast overwhelming, it is important to make fast as well as accurate response to DDoS attacks, and the real-time performance can be even more important to prevent and legitimate the attacks. Among the methods proposed by researchers, the entropy-based detection method provides a sensitive and reliable performance. However, the balance between computational complexity and recognition accuracy remains a challenge. In this paper, we propose a detection method that consists of 3 main parts in different aspects: a sliding time window to fasten the entropy calculation, a single-directional filter to realize early detection during the DDoS progress but not after the crash, and a quintile deviation check algorithm to optimize the detection result. These will eventually lead to a real-time and high-efficient performance to recognize IoT DDoS attacks as soon as possible.
- Subjects :
- Scheme (programming language)
General Computer Science
Computational complexity theory
Computer science
IoT security
Crash
Denial-of-service attack
02 engineering and technology
Computer security
computer.software_genre
0202 electrical engineering, electronic engineering, information engineering
Entropy (information theory)
General Materials Science
joint entropy
computer.programming_language
08 Information and Computing Sciences, 09 Engineering, 10 Technology
business.industry
020208 electrical & electronic engineering
General Engineering
020206 networking & telecommunications
sliding time window
real-time detection
Filter (video)
quintile deviation check
DDoS detection
lcsh:Electrical engineering. Electronics. Nuclear engineering
Internet of Things
business
Cyberspace
computer
lcsh:TK1-9971
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....1edeb51ab37720d98594d1b38190f61c