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http://dx.doi.org/10.13089/JKIISC.2005.15.2.13

Host Anomaly Detection of Neural Networks and Neural-fuzzy Techniques with Soundex Algorithm  

Cha, Byung-Rae (Honam University)
Kim, Hyung-Jong (Honam University)
Park, Bong-Gu (Honam University)
Cho, Hyug-Hyun (Yosu University)
Publication Information
Journal of the Korea Institute of Information Security & Cryptology / v.15, no.2, 2005 , pp. 13-22 More about this Journal
Abstract
To improve the anomaly IDS using system calls, this study focuses on Neural Networks Learning using the Soundex algorithm which is designed to change feature selection and variable length data into a fixed length learning pattern. That is, by changing variable length sequential system call data into a fixed length behavior pattern using the Soundex algorithm, this study conducted neural networks learning by using a backpropagation algorithm with fuzzy membership function. The back-propagation neural networks and Neuro-Fuzzy technique are applied for anomaly intrusion detection of system calls using Sendmail Data of UNM to demonstrate its aspect of he complexity of time, space and MDL performance.
Keywords
Host anomaly detection; Neural Networks; Neuro-Fuzzy; and Soundex algorithm;
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