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Effective Intrusion Detection using Evolutionary Neural Networks  

Han Sang-Jun (연세대학교 컴퓨터학과)
Cho Sung-Bae (연세대학교 컴퓨터학과)
Publication Information
Journal of KIISE:Information Networking / v.32, no.3, 2005 , pp. 301-309 More about this Journal
Abstract
Learning program's behavior using machine learning techniques based on system call audit data is an effective intrusion detection method. Rule teaming, neural network, statistical technique, and hidden Markov model are representative methods for intrusion detection. Among them neural networks are known for its good performance in teaming system call sequences. In order to apply it to real world problems successfully, it is important to determine their structure. However, finding appropriate structure requires very long time because there are no formal solutions for determining the structure of networks. In this paper, a novel intrusion detection technique using evolutionary neural networks is proposed. Evolutionary neural networks have the advantage that superior neural networks can be obtained in shorter time than the conventional neural networks because it leams the structure and weights of neural network simultaneously Experimental results against 1999 DARPA IDEVAL data confirm that evolutionary neural networks are effective for intrusion detection.
Keywords
intrusion detection system; anomaly detection; genetic algorithm; evolutionary neural networks;
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