Semi-Naïve Bayesian method for network intrusion detection system

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5863 LNCS, Issue PART 1, 2009, Pages 614-621

  Panda, Mrutyunjaya ^a   Patra, Manas Ranjan ^b  

^a GIET UNIVERSITY   (India)

^b BERHAMPUR UNIVERSITY   (India)

Author keywords

Accuracy; Ensemble approach; Hybrid DTNB; Intrusion Detection; Rule Based Classifiers

Indexed keywords

ACCESS NETWORK; ACCURACY; BAYESIAN APPROACHES; BAYESIAN METHODS; CLASSIFICATION TASKS; DATA MINING TECHNIQUES; ENSEMBLE APPROACH; FALSE POSITIVE RATES; INTRUSION DETECTION SYSTEMS; NETWORK INTRUSION DETECTION SYSTEMS; RULE-BASED CLASSIFIER;

BAYESIAN NETWORKS; CLASSIFIERS; COMPUTER CRIME; DATA MINING; DATA PROCESSING; DECISION TABLES;

INTRUSION DETECTION;

EID: 76649097981     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-10677-4_70     Document Type: Conference Paper

References (16)

1. MIT Lincoln Laboratory, http://www.ll.mit.edu/IST/ideval/
2. Annur, N.B., Sallehudin, H., Gani, A., Zakari, O.: Identifying false alarm for network intrusion detection system using hybrid data mining and decision tree. Malaysian journal of computer science 21(2), 101-115 (2008)
3. Peddabachigari, S., Abraham, A., Grosan, C., Thomas, J.: Modelling IDS using hybrid intelligent systems. Journal of network and computer applications 30(1), 114-132 (2007)
4. Mukkamala, S., Sung, A.H., Abraham, A.: Intrusion detection using an ensemble of intelligent paradigms. Journal of network and computer applications 28(2005), 167-182 (2005)
5. Pan, Z.-S., Chen, S.-C., Hu, G.-B., Zhang, D.-Q.: Hybrid neural network and C4.5 for Misuse detection. In: Proc. of International conference on Machine Learning and Cybernatics, Xi'an, November 2-5, pp. 2463-2467. IEEE Press, USA (2003)
6. Kotsiantis, S.B.: Supervised machine learning: A review of classification Techniques. Informatica 31, 249-268 (2007)
7. Stein, G., Chen, B., Wu, A.S., Hua, K.A.: Decision Tree classifier for network intusion detection with GA-based feature selection. In: Proc. of the 43rd Annual South East Regional Conference, kennesa, Georgia, vol. 12, pp. 136-141 (2005)
8. Katar, C.: Combining multiple techniques for intrusion detection. Intl. Journal of Comp. Sc and Net. Security (IJCSNS) 6(2B), 208-218 (2006)
9. Salzberg, S.: A nearest hyperrectangle learning method. Machine learning 6, 277-309 (1991)
10. Roy, S.: Nearest Neighbour with generalization, Christchurch, NZ (2002)
11. Cohen, W.W.: Fast effective rule induction. In: 12th Intl. Conf. On Machine learning, pp. 115-123 (1995)
12. Gaines, B.R., Cronpton, P.: Induction of Ripple-Down rules applied to modelling large databases. Journal of Intelligent information system 5(3), 221-228 (1995)
13. Panda, M., Patra, M.R.: Ensembling rule based classifiers for detecting network intrusions. In: International conference on advances in recent techniques communication techniques (ARTCOM 2009), Kerla, India. IEEE Computer Society Press, USA (2009)
14. Russel, S.J., Norvig, P.: Artificial Intelligence: A modern approach. International Edition. Pearson US Imports and PHIPES, London (2002)
15. Domingos, P., Pizzani, M.J.: On the optimality of the simple Bayesian classifier under zero-one loss. Mach.learning 29(2-3), 103-130 (1997)
16. Hall, M., Frank, E.: Combining Naïve Bayes and Decision Tables. In: Wilson, D.L., Chad, H. (eds.) Proc. of the 21st Intl. Florida Artificial Intelligence society conference (FLAIRS), pp. 318-319. AAAI Press, Menlo Park (2008)