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Detection of distributed denial of service attacks using statistical pre-processor and unsupervised neural networks

Jalili, R ; Sharif University of Technology | 2005

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  1. Type of Document: Article
  2. DOI: 10.1007/978-3-540-31979-5_17
  3. Publisher: Springer Verlag , 2005
  4. Abstract:
  5. Although the prevention of Distributed Denial of Service (DDoS) attacks is not possible, detection of such attacks plays main role in preventing their progress. In the flooding attacks, especially new sophisticated DDoS, the attacker floods the network traffic toward the target computer by sending pseudo-normal packets. Therefore, multi-purpose IDSs do not offer a good performance (and accuracy) in detecting such kinds of attacks. In this paper, a novel method for detection of DDoS attacks has been introduced based on a statistical pre-processor and an unsupervised artificial neural net. In addition, SPUNNID system has been designed based on the proposed method. The statistical pre-processing has been used to extract some statistical features of the traffic, showing the behavior of DDoS attacks. The unsupervised neural net is used to analyze and classify them as either a DDoS attack or normal. Moreover, the method has been more investigated using attacked network traffic, which has been provided from a real environment. The experimental results show that SPUNNID detects DDoS attacks accurately and efficiently. © Springer-Verlag Berlin Heidelberg 2005
  6. Keywords:
  7. Neural networks ; Program processors ; Statistical methods ; Telecommunication traffic ; DoS attacks ; Intrusion detection systems ; Statistical pre-processors ; Unsupervised neural nets ; Security of data
  8. Source: First International Conference on Information Security, Practice and Experience, ISPEC 2005, 11 April 2005 through 14 April 2005 ; Volume 3439 , 2005 , Pages 192-203 ; 03029743 (ISSN)
  9. URL: https://link.springer.com/chapter/10.1007/978-3-540-31979-5_17