Abstract
Due to the numerous attacks over the Internet, several early detection systems have been developed to prevent the network from huge losses. Data mining, soft computing, and machine learning are employed to classify historical network traffic whether anomaly or normal. This paper presents the experimental result of network anomaly detection using particle swarm optimization (PSO) for attribute selection and the ensemble of tree-based classifiers (C4.5, Random Forest, and CART) for classification task. Proposed detection model shows the promising result with detection accuracy and lower positive rate compared to existing ensemble techniques.
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References
Liao, H., Lin, C., Lin, Y., Tung, K.: Intrusion detection system: A comprehensive review. Journal of Network and Computer Applications 36(1), 16–24 (2013)
Catania, C., Garino, C.: Automatic network intrusion detection: Current techniques and open issues. Computers & Electrical Engineering 38, 1062–1072 (2012)
Denning, D.: An intrusion-detection model. IEEE Transactions on Software Engineering 2, 222–232 (1987)
Kolias, C., Kambourakis, G., Maragoudakis, M.: Swarm intelligence in intrusion detection: A survey. Computers & Security 30(8), 625–642 (2011)
Moraglio, A., Di Chio, C., Poli, R.: Geometric particle swarm optimisation. In: Genetic Programming. LNCS, vol. 4445. Springer (2007)
Quinlan, J.: C4. 5: programs for machine learning. Elsevier (1993)
Breiman, L.: Random Forests. Machine Learning 45(1), 5–32 (2001)
Breiman, L., Friedman, J., Stone, C., Olshen, R.: Classification and regression trees. CRC Press (1984)
Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)
Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Annals of Statistics 95(2), 337–407 (2000)
Webb, G.: MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning 40(2), 159–196 (2000)
Rodriguez, J., Kuncheva, L., Alonso, C.: Rotation forest: A new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(10), 1619–1630 (2006)
Williamson, M: Biologically Inspired Approaches to Computer Security. Technical Report, HP Laboratories, Bristol (2002)
Twycross, J., Aickelin, U.: An immune-inspired approach to anomaly detection. In: Handbook of Research on Information Security and Assurance. IGI Global (2008)
Zainal, A., Maarof, M., Shamsuddin, S.: Feature selection using rough-DPSO in anomaly intrusion detection. In: Computational Science and Its Applications. LNCS, vol. 4705, pp. 512–524 (2007)
Chung, Y., Wahid, N.: A hybrid network intrusion detection system using simplified swarm optimization (SSO). Applied Soft Computing 12(9), 3014–3022 (2012)
Kennedy, J., Eberhart, R.: A discrete binary version of the particle swarm algorithm. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 4104–4108 (1997)
Hall, M.: Correlation-based feature selection for machine learning. The University of Waikato, Hamilton (1999)
Kuncheva, L.: Combining pattern classifiers: methods and algorithms. John Wiley and Sons (2004)
Kittler, J., Hatef, M., Duin, R., Matas, J.: On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3), 226–239 (1998)
Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.: A detailed analysis of the KDD CUP 99 data set. In: Second IEEE Symposium on Computational Intelligence for Security and Defence Applications (2009)
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Tama, B.A., Rhee, K.H. (2015). A Combination of PSO-Based Feature Selection and Tree-Based Classifiers Ensemble for Intrusion Detection Systems. In: Park, DS., Chao, HC., Jeong, YS., Park, J. (eds) Advances in Computer Science and Ubiquitous Computing. Lecture Notes in Electrical Engineering, vol 373. Springer, Singapore. https://doi.org/10.1007/978-981-10-0281-6_71
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DOI: https://doi.org/10.1007/978-981-10-0281-6_71
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