Intrusion Detection Method Based on Wavelet Neural Network
Aimed at the intrusion behaviors are characterized with uncertainty, complexity, diversity and dynamic tendency and the advantages of wavelet neural network (WNN), an intrusion detection method based on WNN is presented in this paper. Moreover, we adopt a algorithm of reduce the number of the wavele...
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| creator | Jianjing Sun Han Yang Jingwen Tian Fan Wu |
| description | Aimed at the intrusion behaviors are characterized with uncertainty, complexity, diversity and dynamic tendency and the advantages of wavelet neural network (WNN), an intrusion detection method based on WNN is presented in this paper. Moreover, we adopt a algorithm of reduce the number of the wavelet basic function by analysis the sparseness property of sample data which can optimize the wavelet network in a large extent, and the learning algorithm based on the gradient descent was used to train network. We discussed and analyzed the impact factor of intrusion behaviors. With the ability of strong nonlinear function approach and fast convergence rate of WNN, the intrusion detection method based on WNN can detect various intrusion behaviors rapidly and effectively by learning the typical intrusion characteristic information. The experimental result shows that this intrusion detection method is feasible and effective. |
| doi_str_mv | 10.1109/WKDD.2009.214 |
| format | Conference Proceeding |
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Moreover, we adopt a algorithm of reduce the number of the wavelet basic function by analysis the sparseness property of sample data which can optimize the wavelet network in a large extent, and the learning algorithm based on the gradient descent was used to train network. We discussed and analyzed the impact factor of intrusion behaviors. With the ability of strong nonlinear function approach and fast convergence rate of WNN, the intrusion detection method based on WNN can detect various intrusion behaviors rapidly and effectively by learning the typical intrusion characteristic information. The experimental result shows that this intrusion detection method is feasible and effective.</description><identifier>ISBN: 9780769535432</identifier><identifier>ISBN: 0769535437</identifier><identifier>DOI: 10.1109/WKDD.2009.214</identifier><identifier>LCCN: 2008942391</identifier><language>eng</language><publisher>IEEE</publisher><subject>Artificial intelligence ; Artificial neural networks ; Convergence ; Data mining ; Information security ; intrusion behaviors ; Intrusion detection ; network security ; Neural networks ; Uncertainty ; Wavelet analysis ; wavelet neural network ; Wavelet transforms</subject><ispartof>2009 Second International Workshop on Knowledge Discovery and Data Mining, 2009, p.851-854</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4772068$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4772068$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Jianjing Sun</creatorcontrib><creatorcontrib>Han Yang</creatorcontrib><creatorcontrib>Jingwen Tian</creatorcontrib><creatorcontrib>Fan Wu</creatorcontrib><title>Intrusion Detection Method Based on Wavelet Neural Network</title><title>2009 Second International Workshop on Knowledge Discovery and Data Mining</title><addtitle>WKDD</addtitle><description>Aimed at the intrusion behaviors are characterized with uncertainty, complexity, diversity and dynamic tendency and the advantages of wavelet neural network (WNN), an intrusion detection method based on WNN is presented in this paper. Moreover, we adopt a algorithm of reduce the number of the wavelet basic function by analysis the sparseness property of sample data which can optimize the wavelet network in a large extent, and the learning algorithm based on the gradient descent was used to train network. We discussed and analyzed the impact factor of intrusion behaviors. With the ability of strong nonlinear function approach and fast convergence rate of WNN, the intrusion detection method based on WNN can detect various intrusion behaviors rapidly and effectively by learning the typical intrusion characteristic information. The experimental result shows that this intrusion detection method is feasible and effective.</description><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Convergence</subject><subject>Data mining</subject><subject>Information security</subject><subject>intrusion behaviors</subject><subject>Intrusion detection</subject><subject>network security</subject><subject>Neural networks</subject><subject>Uncertainty</subject><subject>Wavelet analysis</subject><subject>wavelet neural network</subject><subject>Wavelet transforms</subject><isbn>9780769535432</isbn><isbn>0769535437</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotTMtOwzAQtIQqASVHTlzyAwl-28sNGgoVBS6gHqtNshaB0KDEBfH3GMFcZkbzYOxU8FIIDuebu6oqJedQSqEPWAbOc2fBKKOVnLHjFHnQUoE4ZNk0vfIEI1NBHrGL1S6O-6kbdnlFkZr4q-4pvgxtfoUTtXnyG_yknmL-QPsR-0TxaxjfTtgsYD9R9s9z9ry8flrcFuvHm9Xicl10wplYIEgJTgLURjUCrbIEFLjwTdvoQDW11qsAIDwabQMCWtEE5TWKoLmv1Zyd_f12RLT9GLt3HL-32jnJ0_IHwfFHUQ</recordid><startdate>200901</startdate><enddate>200901</enddate><creator>Jianjing Sun</creator><creator>Han Yang</creator><creator>Jingwen Tian</creator><creator>Fan Wu</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200901</creationdate><title>Intrusion Detection Method Based on Wavelet Neural Network</title><author>Jianjing Sun ; Han Yang ; Jingwen Tian ; Fan Wu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-a92297299b53c1a636e9ef018cdc4febed683f9918a546fa9a61cf384a1f408b3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Artificial intelligence</topic><topic>Artificial neural networks</topic><topic>Convergence</topic><topic>Data mining</topic><topic>Information security</topic><topic>intrusion behaviors</topic><topic>Intrusion detection</topic><topic>network security</topic><topic>Neural networks</topic><topic>Uncertainty</topic><topic>Wavelet analysis</topic><topic>wavelet neural network</topic><topic>Wavelet transforms</topic><toplevel>online_resources</toplevel><creatorcontrib>Jianjing Sun</creatorcontrib><creatorcontrib>Han Yang</creatorcontrib><creatorcontrib>Jingwen Tian</creatorcontrib><creatorcontrib>Fan Wu</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Jianjing Sun</au><au>Han Yang</au><au>Jingwen Tian</au><au>Fan Wu</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Intrusion Detection Method Based on Wavelet Neural Network</atitle><btitle>2009 Second International Workshop on Knowledge Discovery and Data Mining</btitle><stitle>WKDD</stitle><date>2009-01</date><risdate>2009</risdate><spage>851</spage><epage>854</epage><pages>851-854</pages><isbn>9780769535432</isbn><isbn>0769535437</isbn><abstract>Aimed at the intrusion behaviors are characterized with uncertainty, complexity, diversity and dynamic tendency and the advantages of wavelet neural network (WNN), an intrusion detection method based on WNN is presented in this paper. Moreover, we adopt a algorithm of reduce the number of the wavelet basic function by analysis the sparseness property of sample data which can optimize the wavelet network in a large extent, and the learning algorithm based on the gradient descent was used to train network. We discussed and analyzed the impact factor of intrusion behaviors. With the ability of strong nonlinear function approach and fast convergence rate of WNN, the intrusion detection method based on WNN can detect various intrusion behaviors rapidly and effectively by learning the typical intrusion characteristic information. The experimental result shows that this intrusion detection method is feasible and effective.</abstract><pub>IEEE</pub><doi>10.1109/WKDD.2009.214</doi><tpages>4</tpages></addata></record> |
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| identifier | ISBN: 9780769535432 |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial intelligence Artificial neural networks Convergence Data mining Information security intrusion behaviors Intrusion detection network security Neural networks Uncertainty Wavelet analysis wavelet neural network Wavelet transforms |
| title | Intrusion Detection Method Based on Wavelet Neural Network |
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