Abstract
An Intrusion Detection System (IDS) monitors the system events and examines the log files in order to detect the security problem. In this paper, we analyze the classification algorithms, especially Entropy based classification, Naïve classifier, and J48 using KDD-CUP’99 dataset to detect the different types of attacks. The KDD-Cup’99 dataset is a standard dataset for analysing these type of classification techniques. In KDD-CUP’99 dataset, each instance corresponds to either attack or normal connection. The KDD-Cup’99 dataset contains mainly four types of attack, namely, DOS, U2R, R2L, Probe and these four types of attacks also have subcategories attacks. In this paper, we carry out simulations on the KDD-Cup’99 dataset for all four types of attacks and their subcategories. The back, land, Neptune, pod, smurf, teardrop belong to DoS; the rootkit, Perl, loadmodule, buffer-overflow belong to U2R; the FTP-write, spy, phf, guess-passwd, imap, warezclient, warezmaster, multihop belong to R2L, and the Ipsweep, nmap, portsweep, satan belong to the probe. The simulation results show that the entropy based classification algorithm gives high detection rate and accuracy for normal instances over the J48 and Naïve Bayes classifiers.
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References
Denning, Dorothy E.: An intrusion-detection model. Software Engineering, IEEE Transactions on 2, pp. 222–232. (1987)
Deepa, A. J., V. Kavitha.: A comprehensive survey on approaches to intrusion detection system. Procedia Engineering 38, pp. 2063–2069. (2012)
Mukherjee, Saurabh, Neelam Sharma.: Intrusion detection using naive Bayes classifier with feature reduction. Procedia Technology 4, pp. 119–128. (2012)
Arora, Rohit, Suman Suman.: Comparative analysis of classification algorithms on different datasets using WEKA. International Journal of Computer Applications 54.13, pp. 21–25, (2012)
Sunil Kumar Gautam, Hari Om.: Anomaly Detection System using Entropy based Technique. International Conference on Next Generation Computing Technologies, IEEE, (2015)
Liao, Hung-Jen, et al.: Intrusion detection system: A comprehensive review. Journal of Network and Computer Applications 36.1, pp. 16–24. (2013)
Bolon-Canedo et al.: Feature selection and classification in multiple class datasets: An application to KDD Cup 99 dataset. Expert Systems with Applications 38.5, pp. 5947–5957. (2011)
Guyon et al.: Eds. Feature extraction foundations and applications. Vol. 207. Springer, (2008)
Singh, Rajdeep et al.: Analysis of Feature Selection Techniques for Network Traffic Dataset. Machine Intelligence and Research Advancement (ICMIRA), 2013 International Conference on. IEEE, (2013)
Liu, Weili, Dexian Zhang.: Feature subset selection based on improved discrete particle swarm and support vector machine algorithm. Information Engineering and Computer Science, 2009. ICIECS 2009. International Conference on. IEEE, (2009)
Bu, Hualong, Shangzhi Zheng, Jing Xia.: Genetic algorithm based Semi-feature selection method. Bioinformatics, Systems Biology and Intelligent Computing, 2009. IJCBS’09. International Joint Conference on. IEEE, (2009)
Hall M.A.: Correlation-based feature subset selection for machine learning. Doctorate dissertation, Department of Computer Science, University of Waikato, Hamilton, New Zealand, (1999)
Kumar Ajay, Zhang David.: Personal Recognition Using Hand Shape and Texture. IEEE Transaction on Image Processing, Vol. 15, No. 8, pp. 2454–2461, (2006)
Liu, Huan, Rudy Setiono.: Chi2: Feature selection and discretization of numeric attributes. Tai. IEEE, (1995)
Cantú-Paz Erick, Newsam Shawn, Kamath Chandrika.: Feature selection in scientific applications. 10th ACM SIGKDD international conference on Knowledge discovery and data mining, Seattle, WA, USA, pp. 788–793, (2004)
Zexuan Zhu.: Wrapper–Filter Feature Selection Algorithm Using a Memetic Framework, IEEE Transactions on Systems, Man, and Cybernetics—Part b: Cybernetics, Vol. 37, No. 1, pp. 70–76,(2007)
Handl Julia, Knowles Joshua.: Feature Subset Selection in Unsupervised Learning via Multi objective Optimization. International Journal of Computational Intelligence Research, Vol. 2, No. 3, pp. 217–238, (2006)
Chandolikar, Mrs NS, V. D. Nandavadekar.: Efficient algorithm for intrusion attack classification by analyzing KDD Cup 99. Wireless and Optical Communications Networks (WOCN). 2012 Ninth International Conference on. IEEE, (2012)
Kaur, Gaganjot, and Amit Chhabra.: Improved J48 Classification Algorithm for the Prediction of Diabetes. International Journal of Computer Applications 98.22 (2014)
Gupta, D. L., A. K. Malviya, Satyendra Singh.: Performance analysis of classification tree learning algorithms. IJCA) International Journal of Computer Applications 55.6 (2012)
Hsu, Chung-Chian, Yan-Ping Huang, Keng-Wei Chang.: “Extended Naive Bayes classifier for mixed data.” Expert Systems with Applications 35.3, pp. 1080–1083, (2008)
Farid, Dewan Md, et al.: “Hybrid decision tree and naive Bayes classifiers for multi-class classification tasks.” Expert Systems with Applications 41.4, pp. 1937–1946, (2014)
Chandra, B., Manish Gupta.: Robust approach for estimating probabilities in Naïve–Bayes Classifier for gene expression data. Expert Systems with Applications 38.3, pp. 1293–1298, (2011)
Baron, Grzegorz.: “Influence of Data Discretization on Efficiency of Bayesian Classifier for Authorship Attribution.” Procedia Computer Science 35, pp. 1112–1121, (2011)
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Gautam, S.K., Hari Om (2017). Comparative Analysis of Classification Techniques in Network Based Intrusion Detection Systems. In: Mandal, J., Satapathy, S., Sanyal, M., Bhateja, V. (eds) Proceedings of the First International Conference on Intelligent Computing and Communication. Advances in Intelligent Systems and Computing, vol 458. Springer, Singapore. https://doi.org/10.1007/978-981-10-2035-3_60
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DOI: https://doi.org/10.1007/978-981-10-2035-3_60
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