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Application of SVM and ANN for intrusion detection [An article from: Computers and Operations Research]
Book Details
Author(s)W.-H. Chen, S.-H. Hsu, H.-P. Shen
PublisherElsevier
ISBN / ASINB000RR7RG6
ISBN-13978B000RR7RG1
AvailabilityAvailable for download now
Sales Rank99,999,999
MarketplaceUnited States 🇺🇸
Description
This digital document is a journal article from Computers and Operations Research, published by Elsevier in . The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
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 tfxidf scheme are used to detect potential system intrusions in this study. Our results show that SVM with tfxidf 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.
Description:
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 tfxidf scheme are used to detect potential system intrusions in this study. Our results show that SVM with tfxidf 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.
