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Volumn 2005, Issue , 2005, Pages 51-56

Multi-agent intrusion detection system in industrial network using ant colony clustering approach and unsupervised feature extraction

Author keywords

Clustering; Feature extraction; Industrial network security; Intrusion detection; Swarm intelligence

Indexed keywords

COMPUTER NETWORKS; CONTROL SYSTEMS; DECENTRALIZED CONTROL; FEATURE EXTRACTION; INDUSTRIAL APPLICATIONS; MATHEMATICAL MODELS; SECURITY OF DATA;

EID: 33847299487     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIT.2005.1600609     Document Type: Conference Paper
Times cited : (101)

References (5)
  • 3
    • 23144460461 scopus 로고    scopus 로고
    • Strategies for the increased robustness of ant-based clustering
    • Engineering Self-Organising Systems, Springer-Verlag Heidelberg
    • J. Handl, J. Knowles, and M. Dorigo, "Strategies for the increased robustness of ant-based clustering," In: Engineering Self-Organising Systems, Vol. 2977, LNCS, Springer-Verlag Heidelberg, 2004, pp. 90-104.
    • (2004) LNCS , vol.2977 , pp. 90-104
    • Handl, J.1    Knowles, J.2    Dorigo, M.3
  • 5
    • 1642355954 scopus 로고    scopus 로고
    • Application of machine learning algorithms to KDD intrusion detection dataset within misuse detection context
    • Las Vegas, Nevadat, USA, CSREA Press
    • S. Maheshkumar, and S. Gursel, "Application of machine learning algorithms to KDD intrusion detection dataset within misuse detection context," In: Proc. Int. Conf. on Machine Learning. Models. Technologies and Applications, Las Vegas, Nevadat, USA, CSREA Press, 2003, pp. 209-215.
    • (2003) Proc. Int. Conf. on Machine Learning. Models. Technologies and Applications , pp. 209-215
    • Maheshkumar, S.1    Gursel, S.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.