Data Pre-Processing and Classification for Traffic Anomaly Intrusion Detection Using NSLKDD Dataset

L. Gnanaprasanambikai 1  and Nagarajan Munusamy 1
  • 1 C. M. S. College of Science and Commerce, Bharathier University, Coimbatore, India

Abstract

Network security is essential in the Internet world. Intrusion Detection is one of the network security components. Anomaly Intrusion Detection is a type of intrusion detection that captures the intrinsic characteristics of normal data and uses it in the detection process. To improve the performance of specific anomaly detector selecting the essential features of data and generating a good decision rule is important. The paper we present proposes suitable feature extraction, feature selection and a classification algorithm for traffic anomaly intrusion detection in using NSLKDD dataset. The generated rules of classification process are initial rules of a genetic algorithm.

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  • 1. Gong, R. H., M. Zulkernine, P. Abolmaesumi. A Software Implementation of a Genetic Algorithm Based Approach to Network Intrusion Detection. – In: Proc. of 6th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks, Towson, Maryland, USA, 23-25 May 2005, pp. 246-253.

  • 2. Srinivasa, K. G., N. Pramod. gNIDS: Rule-Based Network Intrusion Detection Systems Using Genetic Algorithms. – International Journal of Intelligent Systems Technologies and Applications, Vol. 11, 2012, Nos 3/4, pp. 252-266.

  • 3. Wu, S. X., W. Banzhaf. The Use of Computational Intelligence in Intrusion Detection System – A Review. – Applied Soft Computing, Vol. 10, 2010, Elseiver, pp. 1-35.

  • 4. Sivanandam, S. N., S. N. Deepa. Introduction to Genetic Algorithms. Springer. ISBN 978-3-540-73189-4.

  • 5. Zargar, G. R., T. Baghaie. Category Based Intrusion Detection Using PCA. – Journal of Information Security, Vol. 3, 2012, pp. 259-271.

  • 6. Neethu, B. Classification of Intrusion Detection Dataset Using Machine Learning Approaches. – International Journal of Electronics and Computer Science Engineering, Vol. V1N3, 2012, pp. 1044-1051.

  • 7. Stein, G., B. Chen, A. S. Wu, K. A. Hua. Decision Tree Classifier for Network Intrusion Detection with GA-Based Feature Selection. – In: Proc. of 43rd Annual Southeast Regional Conference, ACM-SE 43, Vol. 2, 2005, pp. 136-141.

  • 8. Goel, R., A. Sardana, R. C. Joshi. Parallel Misuse and Anomaly Detection Model. – International Journal of Network Security, Vol. 14, July 2012, No 4, pp. 211-222.

  • 9. Patel, B. R., K. K. Rana. A Survey on Decision Tree Algorithm for Classification. – International Journal of Engineering Development and Research, Vol. 2, 2014, Issue 1, pp. 1-5.

  • 10. Davis, J. J., A. J. Clark. Data Preprocessing for Anomaly Based Network Intrusion Detection. – Computer & Security, 2011, Elseiver, pp. 353-375.

  • 11. Thangaraj, M., C. R. Vijayalakshmi. Performance Study on Rule-Based Classification Techniques Across Multiple Database Relations. – International Journal of Applied Information Systems, Vol. 5, March 2013, pp. 1-7. ISSN:2249-0868.

  • 12. Eid, H. F., A. Darwish, A. E. Hassanien, A. Abraham. Principle Component Analysis and Support Vector Machine. – In: Proc. of 10th International Conference on Intelligent Systems Design and Applications, IEEE, 2010, pp. 363-367.

  • 13. Abdullah, B., I. Abd-Alghafar, G. I. Salama, A. Abd-Alhafez. Performance Evaluation of a Genetic Algorithm Based Approach to Network Intrusion Detection. – In: Proc. of 13th International Conference on Aerospace Sciences & Aviation Technology, ASAT-13, 2009, pp. 1-17.

  • 14. Kandeeban, S. S., R. S. Rajesh. A Mutual Construction for IDS Using GA. – International Journal of Advanced Science and Technology, Vol. 29, April 2011, pp. 1-8.

  • 15. Hashemi, V. M., Z. Muda, W. Yassin. Improving Intrusion Detection Using Genetic Algorithm. – Information Technology Journal, Vol. 12, 2013, No 11, pp. 2167-2173.

  • 16. Bhoria, P., D. K. Garg. Determining Feature Set of DOS Attacks. – International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 3, May 2013, Issue 5, pp. 875-878.

  • 17. Vijayarani, S., M. Dhivya. An Efficient Algorithm for Generating Classification Rules. – International Journal of Computer Science and Technology, Vol. 2, October-December 2011, Issue 4, pp. 512-515.

  • 18. Kalyani, G., A. J. Lakshmi. Performance Assessment of Different Classification Techniques for Intrusion Detection. – IOSR Journal of Computer Engineering, Vol. 7, November-December 2012, Issue 5, pp. 25-29.

  • 19. Revathi, S., D. A. Malathi. A Detailed Analysis on NSL-KDD Dataset Using Various Machine Learning Techniques for Intrusion Detection. – International Journal of Engineering Research & Technology (IJERT), Vol. 2, December 2013, Issue 12, pp. 1848-1853.

  • 20. Singh, B. Network Security and Management. PHI Learning Pvt Ltd. Second Edition. 2009.

  • 21. Soman, K. P., S. Diwakar, V. Ajay. Insight into Data Mining Theory and Practice. PHI Learning Pvt Ltd. Third Edition. 2008.

  • 22. Dunham, M. H. Data Mining Introductory and Advanced Topics. Pearson Education, Seventeeth, 2013.

  • 23. Rajesekaran, S., G. A. Vijayalaksmi Pai. Neural Networks, Fuzzy Logic and Genetic Algorithms Synthesis and Applications. PHI, India, 2010.

  • 24. Sumathi, S., S. N. Sivanandam. Data Mining in Security, Studies in Computational Intelligence (SCI). Springer, 2006, pp. 629 -648,

  • 25. Janvier, 2013. http://eric.univlyon2.fr/~ricco/tanagra/fichiers/en_Tanagra_Nb_Components_PCA.pdf

  • 26. Real, E., S. Moore, A. Selle, S. Sexana, Y. L. Suematsu, J. Tan, Q. V. Lie, A. Kurakin. Large-Scale Evolution of Image Classifier. – In: Proc. of International Conference on Machine Learning, 2017.

  • 27. Eibe, F., I. H. Written. Generating Accurate Rulesets without Global Optimization. – In: Proc. of 15 International Conference on Machine Learning, 1998.

  • 28. Rizwan, A., et al. Architecture of Hybrid Mobile Social Networks for Efficient Content Delivery. – Wireless Personal Communications, Vol. 80, 2015, No 1, pp. 85-96.

  • 29. Imran, M., et al. Pseudonym Changing Strategy with Multiple Mix Zones for Trajectory Privacy Protection in Road Networks. – International Journal of Communication Systems, Vol. 31, 2018, No 1, pp. 34-37.

  • 30. Zhao, X., et al. Dimension Reduction of Channel Correlation Matrix Using CUR-Decomposition Technique for 3-D Massive Antenna System. IEEE, Access 6, 2018, pp. 3031-3039.

  • 31. Ezhilarasi, M., V. Krishnaveni. A Survey on Wireless Sensor Network: Energy and Lifetime Perspective. – Taga Journal of Graphic Technology, Vol. 14, 2018.

  • 32. Nagarajan, M., S. Karthikeyan. A New Approach to Increase the Life Time and Efficiency of Wireless Sensor Network. IEEE, 2012.

  • 33. Ezhilarasi, M., V. Krishnaveni. An Optimal Solution to Minimize the Energy Consumption in Wireless Sensor Networks. – International Journal of Pure and Applied Mathematics, Vol. 119, 2018, Issue 10.

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