Application of SVM and ANN for intrusion detection

Authors:
Wun-Hwa Chen;Sheng-Hsun Hsu;Hwang-Pin Shen
Affiliations:
9 ft. Graduate Institute of Business Administration, 1st Building of College of Management, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 106, Taiwan;9 ft. Graduate Institute of Business Administration, 1st Building of College of Management, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 106, Taiwan;9 ft. Graduate Institute of Business Administration, 1st Building of College of Management, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 106, Taiwan
Venue:
Computers and Operations Research
Year:
2005

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Abstract

The popularization of shared networks and Internet usage demands increases attention on information system security, particularly on intrusion detection. Two data mining methodologies--Artificial Neural Networks (ANNs) and Support Vector Machine (SVM) and two encoding methods--simple frequency-based scheme and tf×idf scheme are used to detect potential system intrusions in this study. Our results show that SVM with tf×idf scheme achieved the best performance, while ANN with simple frequency-based scheme achieved the worst. The data used in experiments are BSM audit data from the DARPA 1998 Intrusion Detection Evaluation Program at MIT's Lincoln Labs.