Approach based ensemble methods for better and faster intrusion detection

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

Volumn 6694 LNCS, Issue , 2011, Pages 17-24

  Bahri, Emna ^a   Harbi, Nouria ^a   Huu, Hoa Nguyen ^a  

^a UNIVERSITÉ DE LYON   (France)

Author keywords

boosting; data mining; Ensemble methods; Greedy Boost; intrusion detection systems

Indexed keywords

AUTOMATIC PROCESSING; BOOSTING; CLASSIFIER SYSTEMS; COMBINATION OF MULTIPLE CLASSIFIERS; DATA CLASSIFICATION; DATA SETS; ENSEMBLE METHODS; FALSE ALARMS; GREEDY-BOOST; HUMAN EXPERTISE; INTRUSION DETECTION SYSTEMS; MACHINE LEARNING TECHNIQUES; MULTIPLE CLASSIFIER SYSTEMS; STATE-OF-THE-ART METHODS; ADAPTIVE COMBINATIONS;

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); DATA HANDLING; DATA MINING; INFORMATION SYSTEMS; LEARNING SYSTEMS; MERCURY (METAL); NEURAL NETWORKS;

INTRUSION DETECTION;

EID: 79958193112     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-21323-6_3     Document Type: Conference Paper

References (15)

1. Xiang, C., Yong, P.C., Meng, L.S.: Design of multiple-level hybrid classifier for intrusion detection system using Bayesian clustering and decision trees. Pattern Recognition Letters 29 (2008)
2. Giacinto, G., Roli, F.: Intrusion detection in computer networks by multiple classifier systems. In: 16th International Conference on Pattern Recognition, Quebec City, Canada (2003)
3. Zainal, A., Maarof, M.A., Shamsuddin, S.M., Abraham, A.: Ensemble of one-class classifiers for network intrusion detection system. In: Information Assurance and Security (2008)
4. Perdisci, R., Ariu, D., Fogla, P., Giacinto, G., Lee,W.: McPAD: A multiple classifier system for accurate payload based anomaly detection. Computer Networks 53(6), 864-881 (2009)
5. Giacinto, G., Perdisci, R., Rio, M.D., Roli, F.: Intrusion detection in computer networks by a modular ensemble of one-class classifiers. Information Fusion 9, 69-82 (2008)
6. Giacinto, G., Perdisci, R., Roli, F.: Network Intrusion Detection by Combining One-class Classifiers. In: International Conference on Image Analysis and Processing (2005); Special Session on Intrusion Detection, ICIAP
7. Abadeh, M.S., Habibi, J., Barzegar, Z., Sergi, M.: A parallel genetic local search algorithm for intrusion detection in computer networks. In: Engineering Applications of Artificial Intelligence, vol. 20, pp. 1058-1069 (2007)
8. Mukkamala, S., Sung, A.H., Abraham, A.: Intrusion detection using an ensemble of intelligent paradigms. Network and Computer Applications 28, 167-182 (2005)
9. Zhang, J., Zulkernine, M.: Anomaly Based Network Intrusion Detection with Unsupervised Outlier Detection. In: IEEE International Conference on Communications, ICC 2006, pp. 2388-2393 (2006)
10. DARPA dataset, http://www.ll.mit.edu/mission/communications/ist/corpora/ ideval/data/
11. Shapire, R.: The strength of weak learnability. Machine Learning 5, 197-227 (1990)
12. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to Boosting. J. Comput. Syst. Sci. 55(1), 119-139 (1997)
13. Shafi, K., Abbass, H.A.: An adaptive genetic-based signature learning system for intrusion detection. Expert Systems with Applications 36(10), 12036-12043 (2009)
14. Jiang, S.Y., Song, X., Wang, H., Han, J.J., Li, Q.H.: A clustering-based method for unsupervised intrusion detections. Pattern Recognition Letters 27, 802-810 (2006)
15. Yu, Z., Tsai, J.J.P.: An efficient intrusion detection system using a boosting-based learning algorithm. International Journal of Computer Applications in Technology Achive 27(4) (2006)