Non-stationary Data Mining: The Network Security Issue
Identifieur interne : 001458 ( Main/Exploration ); précédent : 001457; suivant : 001459Non-stationary Data Mining: The Network Security Issue
Auteurs : Sergio Decherchi [Italie] ; Paolo Gastaldo [Italie] ; Judith Redi [Italie] ; Rodolfo Zunino [Italie]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2008.
Abstract
Abstract: Data mining applications explore large amounts of heterogeneous data in search of consistent information. In such a challenging context, empirical learning methods aim to optimize prediction on unseen data, and an accurate estimate of the generalization error is of paramount importance. The paper shows that the theoretical formulation based on the Vapnik-Chervonenkis dimension (d vc ) can be of practical interest when applied to clustering methods for data-mining applications. The presented research adopts the K-Winner Machine (KWM) as a clustering-based, semi-supervised classifier; in addition to fruitful theoretical properties, the model provides a general criterion for evaluating the applicability of Vapnik’s generalization predictions in data mining. The general approach is verified experimentally in the practical problem of detecting intrusions in computer networks. Empirical results prove that the KWM model can effectively support such a difficult classification task and combine unsupervised and supervised.
Url:
DOI: 10.1007/978-3-540-87559-8_4
Affiliations:
Links toward previous steps (curation, corpus...)
- to stream Istex, to step Corpus: 000F40
- to stream Istex, to step Curation: 000F40
- to stream Istex, to step Checkpoint: 000478
- to stream Main, to step Merge: 001478
- to stream Main, to step Curation: 001458
Le document en format XML
<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Non-stationary Data Mining: The Network Security Issue</title><author><name sortKey="Decherchi, Sergio" sort="Decherchi, Sergio" uniqKey="Decherchi S" first="Sergio" last="Decherchi">Sergio Decherchi</name></author><author><name sortKey="Gastaldo, Paolo" sort="Gastaldo, Paolo" uniqKey="Gastaldo P" first="Paolo" last="Gastaldo">Paolo Gastaldo</name></author><author><name sortKey="Redi, Judith" sort="Redi, Judith" uniqKey="Redi J" first="Judith" last="Redi">Judith Redi</name></author><author><name sortKey="Zunino, Rodolfo" sort="Zunino, Rodolfo" uniqKey="Zunino R" first="Rodolfo" last="Zunino">Rodolfo Zunino</name></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:0ADE8409399315E7DD9EBD0F5BC8FAAA2BA516E9</idno><date when="2008" year="2008">2008</date><idno type="doi">10.1007/978-3-540-87559-8_4</idno><idno type="url">https://api.istex.fr/document/0ADE8409399315E7DD9EBD0F5BC8FAAA2BA516E9/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">000F40</idno><idno type="wicri:Area/Istex/Curation">000F40</idno><idno type="wicri:Area/Istex/Checkpoint">000478</idno><idno type="wicri:doubleKey">0302-9743:2008:Decherchi S:non:stationary:data</idno><idno type="wicri:Area/Main/Merge">001478</idno><idno type="wicri:Area/Main/Curation">001458</idno><idno type="wicri:Area/Main/Exploration">001458</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Non-stationary Data Mining: The Network Security Issue</title><author><name sortKey="Decherchi, Sergio" sort="Decherchi, Sergio" uniqKey="Decherchi S" first="Sergio" last="Decherchi">Sergio Decherchi</name><affiliation wicri:level="1"><country xml:lang="fr">Italie</country><wicri:regionArea>Dept. of Biophysical and Electronic Engineering (DIBE), Genoa University, Via Opera Pia 11a, 16145, Genoa</wicri:regionArea><wicri:noRegion>Genoa</wicri:noRegion></affiliation><affiliation wicri:level="1"><country wicri:rule="url">Italie</country></affiliation></author><author><name sortKey="Gastaldo, Paolo" sort="Gastaldo, Paolo" uniqKey="Gastaldo P" first="Paolo" last="Gastaldo">Paolo Gastaldo</name><affiliation wicri:level="1"><country xml:lang="fr">Italie</country><wicri:regionArea>Dept. of Biophysical and Electronic Engineering (DIBE), Genoa University, Via Opera Pia 11a, 16145, Genoa</wicri:regionArea><wicri:noRegion>Genoa</wicri:noRegion></affiliation><affiliation wicri:level="1"><country wicri:rule="url">Italie</country></affiliation></author><author><name sortKey="Redi, Judith" sort="Redi, Judith" uniqKey="Redi J" first="Judith" last="Redi">Judith Redi</name><affiliation wicri:level="1"><country xml:lang="fr">Italie</country><wicri:regionArea>Dept. of Biophysical and Electronic Engineering (DIBE), Genoa University, Via Opera Pia 11a, 16145, Genoa</wicri:regionArea><wicri:noRegion>Genoa</wicri:noRegion></affiliation><affiliation wicri:level="1"><country wicri:rule="url">Italie</country></affiliation></author><author><name sortKey="Zunino, Rodolfo" sort="Zunino, Rodolfo" uniqKey="Zunino R" first="Rodolfo" last="Zunino">Rodolfo Zunino</name><affiliation wicri:level="1"><country xml:lang="fr">Italie</country><wicri:regionArea>Dept. of Biophysical and Electronic Engineering (DIBE), Genoa University, Via Opera Pia 11a, 16145, Genoa</wicri:regionArea><wicri:noRegion>Genoa</wicri:noRegion></affiliation><affiliation wicri:level="1"><country wicri:rule="url">Italie</country></affiliation></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Computer Science</title><imprint><date>2008</date></imprint><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series><idno type="istex">0ADE8409399315E7DD9EBD0F5BC8FAAA2BA516E9</idno><idno type="DOI">10.1007/978-3-540-87559-8_4</idno><idno type="ChapterID">Chap4</idno><idno type="ChapterID">4</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: Data mining applications explore large amounts of heterogeneous data in search of consistent information. In such a challenging context, empirical learning methods aim to optimize prediction on unseen data, and an accurate estimate of the generalization error is of paramount importance. The paper shows that the theoretical formulation based on the Vapnik-Chervonenkis dimension (d vc ) can be of practical interest when applied to clustering methods for data-mining applications. The presented research adopts the K-Winner Machine (KWM) as a clustering-based, semi-supervised classifier; in addition to fruitful theoretical properties, the model provides a general criterion for evaluating the applicability of Vapnik’s generalization predictions in data mining. The general approach is verified experimentally in the practical problem of detecting intrusions in computer networks. Empirical results prove that the KWM model can effectively support such a difficult classification task and combine unsupervised and supervised.</div></front></TEI><affiliations><list><country><li>Italie</li></country></list><tree><country name="Italie"><noRegion><name sortKey="Decherchi, Sergio" sort="Decherchi, Sergio" uniqKey="Decherchi S" first="Sergio" last="Decherchi">Sergio Decherchi</name></noRegion><name sortKey="Decherchi, Sergio" sort="Decherchi, Sergio" uniqKey="Decherchi S" first="Sergio" last="Decherchi">Sergio Decherchi</name><name sortKey="Gastaldo, Paolo" sort="Gastaldo, Paolo" uniqKey="Gastaldo P" first="Paolo" last="Gastaldo">Paolo Gastaldo</name><name sortKey="Gastaldo, Paolo" sort="Gastaldo, Paolo" uniqKey="Gastaldo P" first="Paolo" last="Gastaldo">Paolo Gastaldo</name><name sortKey="Redi, Judith" sort="Redi, Judith" uniqKey="Redi J" first="Judith" last="Redi">Judith Redi</name><name sortKey="Redi, Judith" sort="Redi, Judith" uniqKey="Redi J" first="Judith" last="Redi">Judith Redi</name><name sortKey="Zunino, Rodolfo" sort="Zunino, Rodolfo" uniqKey="Zunino R" first="Rodolfo" last="Zunino">Rodolfo Zunino</name><name sortKey="Zunino, Rodolfo" sort="Zunino, Rodolfo" uniqKey="Zunino R" first="Rodolfo" last="Zunino">Rodolfo Zunino</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Musique/explor/OperaV1/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 001458 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 001458 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Wicri/Musique
|area= OperaV1
|flux= Main
|étape= Exploration
|type= RBID
|clé= ISTEX:0ADE8409399315E7DD9EBD0F5BC8FAAA2BA516E9
|texte= Non-stationary Data Mining: The Network Security Issue
}}
| This area was generated with Dilib version V0.6.21. |  |