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Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Neural Networks for Intrusion Detection

Neural Networks for Intrusion Detection
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Author(s): Rui Ma (Beijing Institute of Technology, China)
Copyright: 2009
Pages: 6
Source title: Encyclopedia of Information Science and Technology, Second Edition
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-60566-026-4.ch447


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With the rapid expansion of computer networks, network security has become a crucial issue for modern computer systems. As an important and active defense technology, the intrusion detection system (IDS) plays an important role in defensive systems. IDSs provide real-time protection from interior attacks, exterior attacks, and invalid operations, and it can intercept intrusions and respond whenever the network system integrity is violated (Ma, 2004). Many intrusion detection approaches have been deeply researched and some widely deployed. But the diversification, complexity, and scale of intrusions raise new demands for IDSs. Neural networks are tolerant of imprecise data and uncertain information. With their inherent ability to generalize from learned data they seem to be an appropriate approach to IDSs (Hofmann, Schmitz, & Sick, 2003). This article discusses the detection of distributed denial-of-service (DDoS) attacks using arti- ficial neural networks techniques. The implementation of a distributed intelligent intrusion detection system (DIIDS) is described, including both the data processing technique and neural networks approaches adopted.

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