메뉴 건너뛰기




Volumn , Issue , 2010, Pages 5-10

Discriminative multinomial naïve bayes for network intrusion detection

Author keywords

Accuracy; Discriminative parameter learning; DMNB; Filtered classifier; Intrusion detection; NSL KDD dataset

Indexed keywords

ACCURACY; DMNB; FILTERED CLASSIFIER; KDD DATASET; PARAMETER LEARNING;

EID: 78349267223     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISIAS.2010.5604193     Document Type: Conference Paper
Times cited : (116)

References (25)
  • 1
    • 10844230994 scopus 로고    scopus 로고
    • Intrusion detection using an ensemble of intelligent paradigms
    • DOI 10.1016/j.jnca.2004.01.003, PII S1084804504000049, Computational Intelligence on the Internet
    • S. Mukkamala, A. H. sung, and Ajith Abraham. Intrusion detection using an ensemble of intelligent paradigms. Journal of Network and Computer Applications. Vol. 28, pp. 167-182, 2005. (Pubitemid 40003209)
    • (2005) Journal of Network and Computer Applications , vol.28 , Issue.2 , pp. 167-182
    • Mukkamala, S.1    Sung, A.H.2    Abraham, A.3
  • 2
    • 78349297778 scopus 로고    scopus 로고
    • Ensemble voting system for anomaly based network intrusion detection
    • M. Panda and M.R. Patra. Ensemble voting system for anomaly based network intrusion detection. International journal of recent trends in engineering, Vol.2, No. 5, pp. 8-13, 2009.
    • (2009) International Journal of Recent Trends in Engineering , vol.2 , Issue.5 , pp. 8-13
    • Panda, M.1    Patra, M.R.2
  • 3
    • 70449915658 scopus 로고    scopus 로고
    • Anomaly based intrusion detection based on junction tree algorithm
    • E. Nikolva and V. Jecheva. Anomaly based intrusion detection based on Junction tree algorithm. Journal of Information assurance and security. Vol. 2, pp. 184-188, 2007.
    • (2007) Journal of Information Assurance and Security , vol.2 , pp. 184-188
    • Nikolva, E.1    Jecheva, V.2
  • 4
    • 33750514606 scopus 로고    scopus 로고
    • Modeling intrusion detection system using hybrid intelligent systems
    • DOI 10.1016/j.jnca.2005.06.003, PII S1084804505000445, Network and Information Security: A Computational Intelligence Approach
    • S. Peddabachigari, A. Abraham, C. Grosan, and J. Thomas. Modeling intrusion detection system using hybrid intelligent systems. Journal of network and computer applications. Vol. 30, pp. 114-132, 2007. (Pubitemid 44666486)
    • (2007) Journal of Network and Computer Applications , vol.30 , Issue.1 , pp. 114-132
    • Peddabachigari, S.1    Abraham, A.2    Grosan, C.3    Thomas, J.4
  • 9
    • 71149086269 scopus 로고    scopus 로고
    • Performance comparison of intrusion detection system classification using various feature reduction techniques
    • V. Venkatechalam and S. selvan, Performance comparison of intrusion detection system classification using various feature reduction techniques. International journal of simulation. Vol. 9, no. 1, pp. 30-39, 2008.
    • (2008) International Journal of Simulation , vol.9 , Issue.1 , pp. 30-39
    • Venkatechalam, V.1    Selvan, S.2
  • 11
    • 78349231539 scopus 로고    scopus 로고
    • Bayesian belief network using genetic local search for detecting network intrusions
    • M. Panda and M.R. Patra, Bayesian belief network using genetic local search for detecting network intrusions. International journal of secure digital information age (IJSDIA), vol. 1, no. 1, pp. 34-44, 2009.
    • (2009) International Journal of Secure Digital Information Age (IJSDIA) , vol.1 , Issue.1 , pp. 34-44
    • Panda, M.1    Patra, M.R.2
  • 12
    • 78349240282 scopus 로고    scopus 로고
    • An evolutionary support vector machine for intrusion detection
    • Sung -Hae Jun and Kyung -whan oh, An evolutionary support vector machine for intrusion detection. Asian journal of information technology, vol. 5, no. 7, pp. 778-783, 2006.
    • (2006) Asian Journal of Information Technology , vol.5 , Issue.7 , pp. 778-783
    • Jun, S.-H.1    Oh, K.-W.2
  • 13
    • 70449353683 scopus 로고    scopus 로고
    • Identifying false alarm for network intrusion detection system using hybrid data mining decision tree
    • N. B. Annur, H. Sallehudin, A. Gani and O. zakari. Identifying false alarm for network intrusion detection system using hybrid data mining decision tree. Malaysian journal of computer science, vol. 21, no. 2, pp. 101-115, 2008.
    • (2008) Malaysian Journal of Computer Science , vol.21 , Issue.2 , pp. 101-115
    • Annur, N.B.1    Sallehudin, H.2    Gani, A.3    Zakari, O.4
  • 17
    • 0031211090 scopus 로고    scopus 로고
    • A decision theoretic generalization of on-line learning and an application to boosting
    • F.Y. Schafire. A decision theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. vol. 55, No. 1, pp. 119-139, 1997.
    • (1997) J. Comput. Syst. Sci. , vol.55 , Issue.1 , pp. 119-139
    • Schafire, F.Y.1
  • 18
    • 0036927090 scopus 로고    scopus 로고
    • Structural extension to logistic regression: Discriminative parameter learning of belief net classifiers
    • R. Reiner and W. Zhou. Structural extension to logistic regression: discriminative parameter learning of belief net classifiers. AAAI/AAI, pp. 167-173, 2002.
    • (2002) AAAI/AAI , pp. 167-173
    • Reiner, R.1    Zhou, W.2
  • 19
    • 31844441688 scopus 로고    scopus 로고
    • Learning bayesian network classifiers by maximizing conditional likelihood
    • ACM press, USA
    • D. Grossman and P. Domingos, Learning Bayesian network classifiers by maximizing conditional likelihood. ICML-2004. p. 46, ACM press, USA.
    • ICML-2004 , pp. 46
    • Grossman, D.1    Domingos, P.2
  • 21
    • 78349246639 scopus 로고    scopus 로고
    • Available at
    • KDDCup 1999 Dataset. Available at: http://kdd.ics.uci.edu/databases/ kddcup1999.html.
  • 22
    • 85019691440 scopus 로고    scopus 로고
    • Testing intrusion detection system: A critique of the 1998 and 1999 DARPA intrusion detection system evaluations as performed by lincoln laboratory
    • J. McHugh. Testing Intrusion detection system: a critique of the 1998 and 1999 DARPA intrusion detection system evaluations as performed by Lincoln Laboratory, ACM Transaction on Information and system security, vol. 3, no. 4, pp. 262-294, 2000.
    • (2000) ACM Transaction on Information and System Security , vol.3 , Issue.4 , pp. 262-294
    • McHugh, J.1
  • 23
    • 78349257690 scopus 로고    scopus 로고
    • Application of variant of AdaBoost based machine learning algorithm in network intrusion detection
    • V.P. Kshirsagar and Dharmaraj R. Patil: Application of Variant of AdaBoost based Machine Learning Algorithm in Network Intrusion Detection. International Journal of Computer Science and Security (IJCSS), Vol. 4, Issue.2, pp. 1-6, 2010.
    • (2010) International Journal of Computer Science and Security (IJCSS) , vol.4 , Issue.2 , pp. 1-6
    • Kshirsagar, V.P.1    Patil, D.R.2
  • 25
    • 33750520151 scopus 로고    scopus 로고
    • D-SCIDS: Distributed soft computing intrusion detection systems
    • Elsevier Science
    • A. Abraham, R. Jain, J. Thomas and S.Y. Han, D-SCIDS: Distributed Soft Computing Intrusion Detection Systems, Journal of Network and Computer Applications, Elsevier Science, Volume 30, Issue 1, pp. 81-98, 2007.
    • (2007) Journal of Network and Computer Applications , vol.30 , Issue.1 , pp. 81-98
    • Abraham, A.1    Jain, R.2    Thomas, J.3    Han, S.Y.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.