Abstract
Increase in the number of Internet users every day leads to the global concern of information security. Intrusion detection plays an important role in protecting data from any intruders that occur in our networks or system. It detects the problem by observing and analyzing the events occurring in the system. Most of the intrusion detection techniques focus on the feature reduction or selection in order to improve the generalization ability. Feature reduction aims at finding the most compact and informative set of features by removing worthless and redundant data or information from the database. The proposed study uses conventional principal component analysis (PCA) with information gain (IG) and chi-square (CHI) for feature reduction. The reduced features are then used to perform classification using support vector classifier. The validation of the proposed approach has been done on the KDD-99 dataset and promising results have been obtained.
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References
Panda M, Abraham A, Patra MR (2012) A hybrid intelligent approach for network intrusion detection. Procedia Eng. 30:1–9
Revathi M, Ramesh T (2011) Network intrusion detection system using reduced dimensionality. Indian J Comput Sci Eng 2(1):61–67
Datti R, Verma B (2010) Feature reduction for intrusion detection using linear discriminant analysis. Int J Eng Sci Technol
Kayacik HG, Zincir-Heywood AN, Heywood MI (2005) Selecting features for intrusion detection: a feature relevance analysis on KDD 99 intrusion detection datasets. In: Proceedings of the third annual conference on privacy, security and trust
Eid HF, Hassanien AE, Kim T, Banerjee S (2013) Linear correlation-based feature selection for network intrusion detection model. In: Advances in security of information and communication networks. Springer, pp. 240–248
Vasan KK, Surendiran B (2016) Dimensionality reduction using principal component analysis for network intrusion detection. Perspect Sci 8:510–512
Thaseen IS, Kumar CA (2017) Intrusion detection model using fusion of chi-square feature selection and multi class SVM. J King Saud Univ Inf Sci 29(4):462–472
Chae H, Jo B, Choi S-H, Park T (2013) Feature selection for intrusion detection using nsl-kdd. Recent Adv Comput Sci 184–187
Lakhina S, Joseph S, Verma B (2010) Feature reduction using principal component analysis for effective anomaly–based intrusion detection on NSL-KDD
Amiri F, Yousefi MR, Lucas C, Shakery A, Yazdani N (2011) Mutual information-based feature selection for intrusion detection systems. J Netw Comput Appl 34(4):1184–1199
Farnaaz N, Jabbar MA (2016) Random forest modeling for network intrusion detection system. Procedia Comput Sci 89:213–217
Manzoor I, Kumar N et al (2017) A feature reduced intrusion detection system using ANN classifier. Expert Syst Appl 88:249–257
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Garg, L., Akashdeep, Aggarwal, N. (2019). A Hybrid Feature Reduced Approach for Intrusion Detection System. In: Peng, SL., Dey, N., Bundele, M. (eds) Computing and Network Sustainability. Lecture Notes in Networks and Systems, vol 75. Springer, Singapore. https://doi.org/10.1007/978-981-13-7150-9_18
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DOI: https://doi.org/10.1007/978-981-13-7150-9_18
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