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1. Identificação
Tipo de ReferênciaArtigo em Evento (Conference Proceedings)
Sitemtc-m16b.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Repositóriosid.inpe.br/mtc-m17@80/2007/12.03.18.35
Última Atualização2007:12.03.18.35.19 (UTC) administrator
Repositório de Metadadossid.inpe.br/mtc-m17@80/2007/12.03.18.35.20
Última Atualização dos Metadados2018:06.05.03.35.54 (UTC) administrator
Chave SecundáriaINPE--PRE/
Chave de CitaçãoGrégioSantMont:2007:EvDaMi
TítuloEvaluation of data mining techniques for suspicious network activity classification using honeypots data
Ano2007
Data de Acesso22 maio 2024
Tipo SecundárioPRE CI
Número de Arquivos1
Tamanho141 KiB
2. Contextualização
Autor1 Grégio, André Ricardo Abed
2 Santos, R.
3 Montes, Antonio
Grupo1 LAC-INPE-MCT-BR
Afiliação1 Instituto Nacional de Pesquisas Espaciais (INPE)
Nome do EventoSPIE: Optics and Photonics 2007; Symposium on Coastal Ocean Remote Sensing, (OP403).
Localização do EventoSan Diego, CA
Data26-30 Aug.
Histórico (UTC)2007-12-03 18:35:20 :: simone -> administrator ::
2012-10-24 00:07:15 :: administrator -> simone :: 2007
2013-02-20 15:20:10 :: simone -> administrator :: 2007
2018-06-05 03:35:54 :: administrator -> marciana :: 2007
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteúdoExternal Contribution
Palavras-ChaveComputer Security
Data Mining
Log Analysis
Intrusion Detection
Artificial Intelligence
ResumoAs the amount and types of remote network services increase, the analysis of their logs has become a very difficult and time consuming task. There are several ways to filter relevant information and provide a reduced log set for analysis, such as whitelisting and intrusion detection tools, but all of them require too much fine- tuning work and human expertise. Nowadays, researchers are evaluating data mining approaches for intrusion detection in network logs, using techniques such as genetic algorithms, neural networks, clustering algorithms, etc. Some of those techniques yield good results, yet requiring a very large number of attributes gathered by network traffic to detect useful information. In this work we apply and evaluate some data mining techniques (K-Nearest Neighbors, Artificial Neural Networks and Decision Trees) in a reduced number of attributes on some log data sets acquired from a real network and a honeypot, in order to classify traffic logs as normal or suspicious. The results obtained allow us to identify unlabeled logs and to describe which attributes were used for the decision. This approach provides a very reduced amount of logs to the network administrator, improving the analysis task and aiding in discovering new kinds of attacks against their networks.
ÁreaCOMP
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Conteúdo da Pasta docacessar
Conteúdo da Pasta sourcenão têm arquivos
Conteúdo da Pasta agreementnão têm arquivos
4. Condições de acesso e uso
URL dos dadoshttp://urlib.net/ibi/sid.inpe.br/mtc-m17@80/2007/12.03.18.35
URL dos dados zipadoshttp://urlib.net/zip/sid.inpe.br/mtc-m17@80/2007/12.03.18.35
Arquivo Alvoevaluation of data.pdf
Grupo de Usuáriosadministrator
simone
Visibilidadeshown
5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/3ESGTTP
Acervo Hospedeirolcp.inpe.br/ignes/2004/02.12.18.39
cptec.inpe.br/walmeida/2003/04.25.17.12
6. Notas
Campos Vaziosarchivingpolicy archivist booktitle callnumber copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi e-mailaddress edition editor electronicmailaddress format identifier isbn issn label language lineage mark mirrorrepository nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarymark serieseditor session shorttitle sponsor subject tertiarymark tertiarytype type url versiontype volume
7. Controle da descrição
e-Mail (login)marciana
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