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Volumn 29, Issue 7, 2008, Pages 918-924

Design of multiple-level hybrid classifier for intrusion detection system using Bayesian clustering and decision trees

Author keywords

Bayesian clustering; Decision tree; False negative; False positive; Intrusion detection system (IDS)

Indexed keywords

BAYESIAN NETWORKS; CLUSTER ANALYSIS; DATA ACQUISITION; DECISION TREES; UNSUPERVISED LEARNING;

EID: 40849099949     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2008.01.008     Document Type: Article
Times cited : (133)

References (28)
  • 1
    • 0027621699 scopus 로고    scopus 로고
    • Agrawal, R., Imielinski, T., Swami, A., 1993. Mining association rules between sets of items in large databases. In: Proc. 1993 ACM SIGMOD Internat. Conf. on Management of Data (SIGMOD'93, Washington, DC, May 26-28), pp. 207-216.
    • Agrawal, R., Imielinski, T., Swami, A., 1993. Mining association rules between sets of items in large databases. In: Proc. 1993 ACM SIGMOD Internat. Conf. on Management of Data (SIGMOD'93, Washington, DC, May 26-28), pp. 207-216.
  • 2
    • 40849086774 scopus 로고    scopus 로고
    • AutoClass C - General Information. .
    • AutoClass C - General Information. .
  • 3
    • 40849116776 scopus 로고    scopus 로고
    • Axelsson, S., 2000. Intrusion detection systems: A taxonomy and survey, Technical Report 99-14, Dept. of Computer Engineering, Chalmers University of Technology, Sweden.
    • Axelsson, S., 2000. Intrusion detection systems: A taxonomy and survey, Technical Report 99-14, Dept. of Computer Engineering, Chalmers University of Technology, Sweden.
  • 4
    • 33845518125 scopus 로고    scopus 로고
    • Bouzida, Y., Cuppens, F., 2006. Detecting known and novel network intrusions. In: Proc. IFIP TC-11 21st Internat. Information Security Conf. (SEC 2006), pp. 258-270.
    • Bouzida, Y., Cuppens, F., 2006. Detecting known and novel network intrusions. In: Proc. IFIP TC-11 21st Internat. Information Security Conf. (SEC 2006), pp. 258-270.
  • 5
    • 0036994338 scopus 로고    scopus 로고
    • Cabrera, J.B.D., Mehra, R.K., 2002. Control and estimation methods in information assurance - a tutorial on intrusion detection systems. In: Proc. 41st IEEE Conf. on Decision and Control, pp. 1402-1407.
    • Cabrera, J.B.D., Mehra, R.K., 2002. Control and estimation methods in information assurance - a tutorial on intrusion detection systems. In: Proc. 41st IEEE Conf. on Decision and Control, pp. 1402-1407.
  • 6
    • 40849132872 scopus 로고    scopus 로고
    • Chan, P.K., Stolfo, S.J., 1993. Toward parallel and distributed learning by meta-learning. In: Proc. AAAI Workshop on Knowledge Discovery in Database, pp. 227-240.
    • Chan, P.K., Stolfo, S.J., 1993. Toward parallel and distributed learning by meta-learning. In: Proc. AAAI Workshop on Knowledge Discovery in Database, pp. 227-240.
  • 7
    • 40849100665 scopus 로고    scopus 로고
    • Cheeseman, P., Kelly, J., Self, M., Stutz, J., Taylor, W., Freeman, D., 1988. AutoClass: A Bayesian classification system. In: Proc. Fifth Internat. Conf. on Machine Learning.
    • Cheeseman, P., Kelly, J., Self, M., Stutz, J., Taylor, W., Freeman, D., 1988. AutoClass: A Bayesian classification system. In: Proc. Fifth Internat. Conf. on Machine Learning.
  • 8
    • 0023294428 scopus 로고
    • An intrusion detection model
    • Denning D. An intrusion detection model. IEEE Trans. Software Eng. 13 2 (1987) 222-232
    • (1987) IEEE Trans. Software Eng. , vol.13 , Issue.2 , pp. 222-232
    • Denning, D.1
  • 9
    • 25844491810 scopus 로고    scopus 로고
    • An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks
    • Depren O., Topllar M., Anarim E., and Ciliz M.K. An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks. Expert Syst. Appl. 29 (2005) 713-722
    • (2005) Expert Syst. Appl. , vol.29 , pp. 713-722
    • Depren, O.1    Topllar, M.2    Anarim, E.3    Ciliz, M.K.4
  • 10
    • 0038330235 scopus 로고    scopus 로고
    • Fusion of multiple classifiers for intrusion detection in computer networks
    • Giacinto G., Roli F., and Didaci L. Fusion of multiple classifiers for intrusion detection in computer networks. Pattern Recognition Lett. 24 (2003) 1795-1803
    • (2003) Pattern Recognition Lett. , vol.24 , pp. 1795-1803
    • Giacinto, G.1    Roli, F.2    Didaci, L.3
  • 11
    • 33847743856 scopus 로고    scopus 로고
    • Hybrid intrusion detection with weighted signature generation over anomalous internet episodes
    • Hwang K., Cai M., Chen Y., and Qin M. Hybrid intrusion detection with weighted signature generation over anomalous internet episodes. IEEE Trans. Depend. Secure Comput. 4 (2007) 41-55
    • (2007) IEEE Trans. Depend. Secure Comput. , vol.4 , pp. 41-55
    • Hwang, K.1    Cai, M.2    Chen, Y.3    Qin, M.4
  • 12
    • 3142623031 scopus 로고    scopus 로고
    • Clustering intrusion detection alarms to support root cause analysis
    • Julisch K. Clustering intrusion detection alarms to support root cause analysis. ACM Trans. Inform. Syst. Security 6 (2003) 443-471
    • (2003) ACM Trans. Inform. Syst. Security , vol.6 , pp. 443-471
    • Julisch, K.1
  • 13
    • 40849088439 scopus 로고    scopus 로고
    • KDD Cup, 1999. Data, Information and Computer Science, University of California, Irvine. .
    • KDD Cup, 1999. Data, Information and Computer Science, University of California, Irvine. .
  • 14
    • 84885774862 scopus 로고    scopus 로고
    • A framework for constructing features and models for intrusion detection systems
    • Lee W., and Stolfo S.J. A framework for constructing features and models for intrusion detection systems. ACM Trans. Inform. Syst. Security 3 4 (2000) 227-261
    • (2000) ACM Trans. Inform. Syst. Security , vol.3 , Issue.4 , pp. 227-261
    • Lee, W.1    Stolfo, S.J.2
  • 15
    • 1642354876 scopus 로고    scopus 로고
    • KDD-99 classifier learning contest LLSoft's results overview
    • Levin I. KDD-99 classifier learning contest LLSoft's results overview. SIGKDD Explor. ACM SIGKDD (2000)
    • (2000) SIGKDD Explor. ACM SIGKDD
    • Levin, I.1
  • 17
    • 40849118704 scopus 로고    scopus 로고
    • Lunt, T., 1988. Automated audit trail analysis and intrusion detection: A survey. In: Proc. 11th National Computer Security Conference, pp. 65-73.
    • Lunt, T., 1988. Automated audit trail analysis and intrusion detection: A survey. In: Proc. 11th National Computer Security Conference, pp. 65-73.
  • 18
    • 40849085752 scopus 로고    scopus 로고
    • Mannilla, H., Toivonen, H., Verkamo, A.I., 1995. Discovering frequent episodes in sequences. In: Proc. 1st Internat. Conf. on Knowledge Discovery in Databases and Data Mining.
    • Mannilla, H., Toivonen, H., Verkamo, A.I., 1995. Discovering frequent episodes in sequences. In: Proc. 1st Internat. Conf. on Knowledge Discovery in Databases and Data Mining.
  • 20
    • 1542285202 scopus 로고    scopus 로고
    • Pan, Z.S., Chen, S.C., Hu, G.B., Zhang, D.Q., 2003. Hybrid neural network and C4.5 for misuse detection. In: Proc. 2003 Internat. Conf. on Machine Learning and Cybernetics, vol. 4, pp. 2463-2467.
    • Pan, Z.S., Chen, S.C., Hu, G.B., Zhang, D.Q., 2003. Hybrid neural network and C4.5 for misuse detection. In: Proc. 2003 Internat. Conf. on Machine Learning and Cybernetics, vol. 4, pp. 2463-2467.
  • 22
    • 33749606368 scopus 로고    scopus 로고
    • Petrovic, S., Alvarez, G., Orfila, A., Carbo, J., 2006. Labelling clusters in an intrusion detection system using a combination of clustering evaluation techniques. In: Proc. 39th Annual Hawaii Internat. Conf. on System Sciences, pp. 129b-129b.
    • Petrovic, S., Alvarez, G., Orfila, A., Carbo, J., 2006. Labelling clusters in an intrusion detection system using a combination of clustering evaluation techniques. In: Proc. 39th Annual Hawaii Internat. Conf. on System Sciences, pp. 129b-129b.
  • 23
    • 0347606556 scopus 로고    scopus 로고
    • Winning the KDD99 classification cup: Bagged boosting
    • Pfahringer B. Winning the KDD99 classification cup: Bagged boosting. SIGKDD Explor. 1 2 (2000) 67-75
    • (2000) SIGKDD Explor. , vol.1 , Issue.2 , pp. 67-75
    • Pfahringer, B.1
  • 25
    • 40849141341 scopus 로고    scopus 로고
    • Weka 3: Data Mining Software in Java, University of Waikato, New Zealand. .
    • Weka 3: Data Mining Software in Java, University of Waikato, New Zealand. .
  • 26
    • 11244249796 scopus 로고    scopus 로고
    • Xiang, C., Chong, M.Y., Zhu, H.L., 2004. Design of multiple-level tree classifiers for intrusion detection system. In: Proc. 2004 IEEE Conf. on Cybernetics and Intelligent Systems, December, Singapore, pp. 872-877.
    • Xiang, C., Chong, M.Y., Zhu, H.L., 2004. Design of multiple-level tree classifiers for intrusion detection system. In: Proc. 2004 IEEE Conf. on Cybernetics and Intelligent Systems, December, Singapore, pp. 872-877.
  • 27
    • 2442526701 scopus 로고    scopus 로고
    • Zanero, S., Savaresi, S.M., 2004. Unsupervised learning techniques for an intrusion detection system. In: Proc. 2004 ACM Symposium on Applied Computing, pp. 412-419.
    • Zanero, S., Savaresi, S.M., 2004. Unsupervised learning techniques for an intrusion detection system. In: Proc. 2004 ACM Symposium on Applied Computing, pp. 412-419.
  • 28
    • 33750955638 scopus 로고    scopus 로고
    • Zhang, J., Zulkernine, M., 2006. A hybrid network intrusion detection technique using random forests. In: Proc. 1st Internat. Conf. on Availability, Reliability and Security, ARES 2006, Vienna, Austria, pp. 262-269.
    • Zhang, J., Zulkernine, M., 2006. A hybrid network intrusion detection technique using random forests. In: Proc. 1st Internat. Conf. on Availability, Reliability and Security, ARES 2006, Vienna, Austria, pp. 262-269.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.