Application of a support vector machine and an artificial neural network for intrusion detection

Application of a support vector machine and an artificial neural network for intrusion detection

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Article ID: iaor20062677
Country: United Kingdom
Volume: 32
Issue: 10
Start Page Number: 2617
End Page Number: 2634
Publication Date: Oct 2005
Journal: Computers and Operations Research
Authors: , ,
Keywords: computers: information, neural networks, statistics: general
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.

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