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Volumn 1, Issue 4, 2006, Pages 8-17

Database intrusion detection using weighted sequence mining

Author keywords

Data dependency; E R Model; Intrusion detection; Weighted sequence mining

Indexed keywords

DATA MINING; INFORMATION MANAGEMENT; MERCURY (METAL); NETWORK SECURITY;

EID: 70350394979     PISSN: 1796203X     EISSN: None     Source Type: Journal    
DOI: 10.4304/jcp.1.4.8-17     Document Type: Article
Times cited : (43)

References (18)
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    • Fayyad, U.1    Shapiro, G.P.2    Smyth, P.3
  • 3
    • 0038223829 scopus 로고    scopus 로고
    • NIST Special Publication on Intrusion Detection System
    • R. Bace, P. Mell, Intrusion Detection System, NIST Special Publication on Intrusion Detection System (2001).
    • (2001) Intrusion Detection System
    • Bace, R.1    Mell, P.2
  • 7
    • 0142253852 scopus 로고    scopus 로고
    • ADAM: A testbed for exploring the use of data mining in intrusion detection
    • D. Barbara, J. Couto, S. Jajodia, N. Wu, ADAM: A Testbed for Exploring the Use of Data Mining in Intrusion Detection, ACM SIGMOD, pp. 15-24 (2001).
    • (2001) ACM SIGMOD , pp. 15-24
    • Barbara, D.1    Couto, J.2    Jajodia, S.3    Wu, N.4
  • 12
    • 6344243160 scopus 로고    scopus 로고
    • Research on algorithm of user query frequent itemsets mining
    • Y. Zhong, X. Qin, Research on Algorithm of User Query Frequent Itemsets Mining, Machine Learning Cybernetics, pp. 1671-1676 (2004).
    • (2004) Machine Learning Cybernetics , pp. 1671-1676
    • Zhong, Y.1    Qin, X.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.