Abstract
Network intrusion detection gained a lot of attention from the security expert. Intrusion detection system has been designed for the purpose detecting attack and comprises of detection method that can be anomaly based or it can be signature based. These detection method, however, highly depends on the quality of the input features. Supervised learning approach for the detection method finds the relationship between the feature and its class. Therefore, irrelevant, redundant, and noisy features must be eliminated before applying supervised algorithm. This can be done by feature selection method. In this paper ant colony optimization has been applied for feature selection on KDD99 dataset. The reduced dataset is validated using support vector machine. Results show that accuracy of the SVM is significantly improved with reduced feature set.
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References
Kenkre PS, Pai A, Colaco L (2015) Real time intrusion detection and prevention system. In: Proceedings of the 3rd international conference on frontiers of intelligent computing: theory and applications (FICTA) 2014, pp 405–411
Bhuyan MH, Bhattacharyya DK, Kalita JK (2014) Network anomaly detection: methods, systems and tools. IEEE Commun Surv Tutorials 16(1):303–336
Othman ZA, Muda Z, Theng LM, Othman MR (2014) Record to record feature selection algorithm for network intrusion detection. Int J Adv Comput Technol 6(2):163
GarcÃa-Teodoro P, DÃaz-Verdejo J, Maciá-Fernández G, Vázquez E (2009) Anomaly-based network intrusion detection: techniques, systems and challenges. Comput Secur 28(1–2):18–28
Hämäläinen T (2014) Artificial immune system based intrusion detection: innate immunity using an unsupervised learning approach
Friedberg I, Skopik F, Fiedler R (2015) Cyber situational awareness through network anomaly detection: state of the art and new approaches. e i Elektrotechnik und Informationstechnik 132(2):101–105
GarcÃa S, Luengo J, Herrera F (2015) Feature selection. In: Data preprocessing in data mining SE—7, vol 72. Springer, pp 163–193
Düntsch I, Gediga G (2000) Rough set data analysis–a road to non-invasive knowledge discovery
Liu H, Yu L (2005) Toward integrating feature selection algorithms for classification and clustering. Knowl Data Eng IEEE Trans 17(4):491–502
Zhang F, Chan PPK, Biggio B, Yeung DS, Roli F (2015) Adversarial feature selection against evasion attacks
Pitt E, Nayak R (2007) The use of various data mining and feature selection methods in the analysis of a population survey dataset. In: Proceedings of the 2nd international workshop on Integrating artificial intelligence and data mining, vol 84, pp 83–93
Wang A, An N, Chen G, Li L, Alterovitz G (2015) Accelerating wrapper-based feature selection with K-nearest-neighbor. Knowl-Based Syst 83:81–91
Rais HM, Othman ZA, Hamdan AR (2007) Improved dynamic ant colony system (DACS) on symmetric traveling salesman problem (TSP). Int Conf Intell Adv Syst ICIAS 2007, pp 43–48
George A (2012) Anomaly detection based on machine learning: dimensionality reduction using PCA and classification using SVM. Int J Comput Appl Vol
Tsang C-H, Kwong S (2005) Multi-agent intrusion detection system in industrial network using ant colony clustering approach and unsupervised feature extraction. In: IEEE international conference on industrial technology ICIT 2005, pp 51–56
Gao H, Yang H, Wang X (2005) Ant colony optimization based network intrusion feature selection and detection, pp 18–21
Abd-Alsabour N, Randall M (2010) Feature selection for classification using an ant colony system. In: 2010 Sixth IEEE international conference on e-Science work, pp 86–91
Hiba Basim Alwan KRK-M (2013) Mixed variable ant colony optimization technique for feature subset selection and model selection, no 025, pp 24–31
Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. ACM SIGKDD Explor Newsl 11:10–18
Tavallaee M, Bagheri E, Lu W, Ghorbani A-A (2009) A detailed analysis of the KDD CUP 99 data set. In: Proceedings of the second IEEE symposium on computational intelligence for security and defence applications 2009
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Mehmod, T., Rais, H.B.M. (2016). Ant Colony Optimization and Feature Selection for Intrusion Detection. In: Soh, P., Woo, W., Sulaiman, H., Othman, M., Saat, M. (eds) Advances in Machine Learning and Signal Processing. Lecture Notes in Electrical Engineering, vol 387. Springer, Cham. https://doi.org/10.1007/978-3-319-32213-1_27
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DOI: https://doi.org/10.1007/978-3-319-32213-1_27
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