Anomaly Detection Performance Analysis of Neural Networks using Soundex Algorithm and N-gram Techniques based on System Calls


Bong-Goo Park, Journal of Information Processing Systems Vol. 6, No. 5, pp. 45-56, Oct. 2005  


Keywords: Anomaly Intrusion Detection, Soundex Algorithm, System Calls, Neural Networks
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Abstract

The weak foundation of the computing environment caused information leakage and hacking to be uncontrollable, Therefore, dynamic control of security threats and real-time reaction to identical or similar types of accidents after intrusion are considered to be important, h one of the solutions to solve the problem, studies on intrusion detection systems are actively being conducted. 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 iength behavior pattern using the soundex algorithm, this study conducted neural networks learning by using a backpropagation algorithm. The backpropagation neural networks technique is applied for anomaly detection of system calls using Sendmail Data of UNM to demonstrate its performance.


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Cite this article
[APA Style]
Park, B. (2005). Anomaly Detection Performance Analysis of Neural Networks using Soundex Algorithm and N-gram Techniques based on System Calls. Journal of Internet Computing and Services, 6(5), 45-56.

[IEEE Style]
B. Park, "Anomaly Detection Performance Analysis of Neural Networks using Soundex Algorithm and N-gram Techniques based on System Calls," Journal of Internet Computing and Services, vol. 6, no. 5, pp. 45-56, 2005.

[ACM Style]
Bong-Goo Park. 2005. Anomaly Detection Performance Analysis of Neural Networks using Soundex Algorithm and N-gram Techniques based on System Calls. Journal of Internet Computing and Services, 6, 5, (2005), 45-56.