메뉴 건너뛰기




Volumn 9, Issue 2, 2009, Pages 462-469

Intrusion detection using fuzzy association rules

Author keywords

Association hyper edge; Association rule; Classification; Intrusion detection; Matching measure

Indexed keywords

ASSOCIATION RULES; ASSOCIATIVE PROCESSING; COMPUTER CRIME; COMPUTER VIRUSES; DATA MINING; EDGE DETECTION; FUZZY RULES; INFORMATION MANAGEMENT;

EID: 58549089680     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2008.06.001     Document Type: Article
Times cited : (191)

References (26)
  • 2
    • 58549096235 scopus 로고    scopus 로고
    • D. Anderson, T.F. Lunt, H. Javits, A. Tamaru, A. Valdes, Detecting unusual program behavior using the statistical components of NIDES, NIDES Technical Report, SRI International, May 1995.
    • D. Anderson, T.F. Lunt, H. Javits, A. Tamaru, A. Valdes, Detecting unusual program behavior using the statistical components of NIDES, NIDES Technical Report, SRI International, May 1995.
  • 3
    • 0343462384 scopus 로고    scopus 로고
    • Determination of fuzzy logic membership functions using genetic algorithms
    • Arslan A., and Kaya M. Determination of fuzzy logic membership functions using genetic algorithms. Fuzzy Sets Syst. 118 2 (2001) 297-306
    • (2001) Fuzzy Sets Syst. , vol.118 , Issue.2 , pp. 297-306
    • Arslan, A.1    Kaya, M.2
  • 4
    • 0035665469 scopus 로고    scopus 로고
    • A comparison of Intrusion Detection Systems
    • Biermann E., Cloete E., and Venter L.M. A comparison of Intrusion Detection Systems. Comput. Security 20 8 (December 2001) 676-683
    • (2001) Comput. Security , vol.20 , Issue.8 , pp. 676-683
    • Biermann, E.1    Cloete, E.2    Venter, L.M.3
  • 5
    • 58549115839 scopus 로고    scopus 로고
    • C. Borgelt, Efficient implementation of Apriori and Elact, Presented at Workshop of Frequent Item Set Mining Implementations FIMI, USA, 2003.
    • C. Borgelt, Efficient implementation of Apriori and Elact, Presented at Workshop of Frequent Item Set Mining Implementations FIMI, USA, 2003.
  • 6
    • 58549111738 scopus 로고    scopus 로고
    • C. Borgelt, R. Kruse, Induction of association rules: Apriori implementation, Presented at 15th Conference on Computational Statistics (Germany, 2002). Available: http://fuzzy.cs.uni-magdeburg.de/∼borgelt/papers/cstat_02.pdf.
    • C. Borgelt, R. Kruse, Induction of association rules: Apriori implementation, Presented at 15th Conference on Computational Statistics (Germany, 2002). Available: http://fuzzy.cs.uni-magdeburg.de/∼borgelt/papers/cstat_02.pdf.
  • 7
    • 58549120701 scopus 로고    scopus 로고
    • C. Borgelt, 2005, Association Rule Induction, Available: http://fuzzy.cs.uni-magdeburg.de/∼borgelt.
    • C. Borgelt, 2005, Association Rule Induction, Available: http://fuzzy.cs.uni-magdeburg.de/∼borgelt.
  • 11
    • 0242637092 scopus 로고    scopus 로고
    • Detecting intrusion with rule-based integration of multiple models
    • Han S.J., and Cho S.B. Detecting intrusion with rule-based integration of multiple models. Comput. Security 22 7 (2003) 613-623
    • (2003) Comput. Security , vol.22 , Issue.7 , pp. 613-623
    • Han, S.J.1    Cho, S.B.2
  • 13
    • 0348132918 scopus 로고    scopus 로고
    • Mining fuzzy association rules in databases
    • Kuok C., Fu A., and Wong M. Mining fuzzy association rules in databases. SIGMOD Record 27 1 (1998) 41-46
    • (1998) SIGMOD Record , vol.27 , Issue.1 , pp. 41-46
    • Kuok, C.1    Fu, A.2    Wong, M.3
  • 16
    • 0036722344 scopus 로고    scopus 로고
    • Mining the optimal Class Association Rule set
    • Li J., Shen H., and Topr R. Mining the optimal Class Association Rule set. Knowledge Based Syst. 15 (2002) 399-405
    • (2002) Knowledge Based Syst. , vol.15 , pp. 399-405
    • Li, J.1    Shen, H.2    Topr, R.3
  • 17
    • 0034300835 scopus 로고    scopus 로고
    • Improving intrusion detection performance using keyword selection and neural networks
    • Lippmann R., and Cunningham S. Improving intrusion detection performance using keyword selection and neural networks. Comput. Netw. 34 4 (2000) 594-603
    • (2000) Comput. Netw. , vol.34 , Issue.4 , pp. 594-603
    • Lippmann, R.1    Cunningham, S.2
  • 19
    • 0000418873 scopus 로고    scopus 로고
    • An overview of membership function generation techniques for pattern recognition
    • Medasani S., Kim J., and Krishnapuram R. An overview of membership function generation techniques for pattern recognition. Int. J. Approx. Reason. 19 3 (1998) 391-417
    • (1998) Int. J. Approx. Reason. , vol.19 , Issue.3 , pp. 391-417
    • Medasani, S.1    Kim, J.2    Krishnapuram, R.3
  • 20
    • 27744549009 scopus 로고    scopus 로고
    • H∞ estimation for fuzzy membership function optimization
    • Simon D. H∞ estimation for fuzzy membership function optimization. International Journal of Approximate Reasoning 40 3 (2005) 224-242
    • (2005) International Journal of Approximate Reasoning , vol.40 , Issue.3 , pp. 224-242
    • Simon, D.1
  • 21
    • 58549108337 scopus 로고    scopus 로고
    • S.J. Stolfo, et al., 1999, KDD-99 dataset, Available: http://www.kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.
    • S.J. Stolfo, et al., 1999, KDD-99 dataset, Available: http://www.kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.
  • 23
    • 58549113499 scopus 로고    scopus 로고
    • A. Tajbakhsh, Design and implementation of an Intrusion Detection Systems using data mining techniques, M.Sc. Thesis, Instructor M. Rahmati, Department of computer engineering, Amirkabir University of Technology, Iran, 2006.
    • A. Tajbakhsh, Design and implementation of an Intrusion Detection Systems using data mining techniques, M.Sc. Thesis, Instructor M. Rahmati, Department of computer engineering, Amirkabir University of Technology, Iran, 2006.
  • 26
    • 58549117035 scopus 로고    scopus 로고
    • H.J. Zimmermann, third ed., Fuzzy Set Theory and its Applications, Kluwer, Boston,1996.
    • H.J. Zimmermann, third ed., Fuzzy Set Theory and its Applications, Kluwer, Boston,1996.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.