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Volumn 17, Issue 2, 2007, Pages 81-91

Training all the KDD data set to classify and detect attacks

Author keywords

Attack categories; Intrusion detection systems; KDD data sets; Misuse detection; Neural networks

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; RADIAL BASIS FUNCTION NETWORKS; SECURITY OF DATA; SELF ORGANIZING MAPS;

EID: 34250213201     PISSN: 12100552     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (12)

References (16)
  • 1
    • 33747630166 scopus 로고    scopus 로고
    • Master's thesis, department of computer science, Rochester institute of technology, Rochester, NY 14623, September
    • Novikov D.: Noural Networks to Intrusion Detection, Master's thesis, department of computer science, Rochester institute of technology, Rochester, NY 14623, September 2005.
    • (2005) Noural Networks to Intrusion Detection
    • Novikov, D.1
  • 3
    • 33845885921 scopus 로고    scopus 로고
    • Network-based intrusion detection using neural networks
    • Rensselaer Polytechnic Institute Troy, Now York 12180-3590
    • Bivens A. at al.: Network-based intrusion detection using neural networks, technical report, Rensselaer Polytechnic Institute Troy, Now York 12180-3590, 2002.
    • (2002) technical report
    • Bivens, A.1    at al2
  • 4
    • 34250195060 scopus 로고    scopus 로고
    • Simple solutions to network-based intrusion detection
    • Department of Computer Sciences, the university of Texas at Austin, Austin, TX, 78705
    • Surdulescu R., Hajra S.: Simple solutions to network-based intrusion detection, technical report, Department of Computer Sciences, the university of Texas at Austin, Austin, TX, 78705, 2004.
    • (2004) technical report
    • Surdulescu, R.1    Hajra, S.2
  • 5
    • 0141464248 scopus 로고    scopus 로고
    • HIDE: A Hierarchical Network Intrusion Detection System Using Statistical Preprocessing and Neural Network Classification
    • United States Military Academy, West Point, NY, 5-6 June
    • Zhang Z. at al.: HIDE: a Hierarchical Network Intrusion Detection System Using Statistical Preprocessing and Neural Network Classification. In: Proceeding of the 2001 IEEE Workshop on Information Assurance and Security, United States Military Academy, West Point, NY, 5-6 June 2001, pp. 85-90.
    • (2001) Proceeding of the 2001 IEEE Workshop on Information Assurance and Security , pp. 85-90
    • Zhang, Z.1    at al2
  • 10
    • 85070035728 scopus 로고    scopus 로고
    • accessed in July 2006
    • KDD data set, 1999; http://kdd.ica.uci.edu/databases/kddcup99/kddcup99. html, accessed in July 2006.
    • (1999)
    • data set, K.D.D.1
  • 11
    • 85070035854 scopus 로고    scopus 로고
    • NeuroSolutions, version 5.03, by Curt Lefebvre and Jose Principe, NeuroDimension Inc, Copyright, 1994-2005. Online support
    • NeuroSolutions, version 5.03, by Curt Lefebvre and Jose Principe, NeuroDimension Inc., Copyright, 1994-2005. Online support: www.nd.com
  • 13
    • 34250185435 scopus 로고    scopus 로고
    • Neural networks learning improvement using the K-means clustering algorithm to detect network intrusions
    • Faraoun K. M., Boukelif A.: Neural networks learning improvement using the K-means clustering algorithm to detect network intrusions, International Journal of Computational Intelligence, 3, 2, 2006, pp. 161-168.
    • (2006) International Journal of Computational Intelligence , vol.3 , Issue.2 , pp. 161-168
    • Faraoun, K.M.1    Boukelif, A.2
  • 14
    • 1642354876 scopus 로고    scopus 로고
    • KDD-99 Classifier Learning Contest LLSoft's Results Overview. SIGKDD Explorations
    • Levin I.: KDD-99 Classifier Learning Contest LLSoft's Results Overview. SIGKDD Explorations. ACM SIGKDD. 1, 2, 2000, pp. 67-75.
    • (2000) ACM SIGKDD , vol.1 , Issue.2 , pp. 67-75
    • Levin, I.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.