https://scholars.lib.ntu.edu.tw/handle/123456789/472562
Title: | Application of SVM and ANN for intrusion detection | Authors: | WUN-HWA CHEN Hsu S.-H. Shen H.-P. |
Keywords: | Artificial neural networks; Intrusion detection; Support vector machine | Issue Date: | 2005 | Journal Volume: | 32 | Journal Issue: | 10 | Start page/Pages: | 2617-2634 | Source: | Computers and Operations Research | 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. © 2004 Elsevier Ltd. All rights reserved. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/472562 | DOI: | 10.1016/j.cor.2004.03.019 | SDG/Keyword: | Computer crime; Computer software; Computer system firewalls; Data mining; Data warehouses; Intellectual property; Internet; Neural networks; Information system security; Intrusion detection; Support vector machines (SVM); Security of data |
Appears in Collections: | 工商管理學系 |
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