Have a personal or library account? Click to login
Data Pre-Processing and Classification for Traffic Anomaly Intrusion Detection Using NSLKDD Dataset Cover

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

Open Access
|Sep 2018

References

  1. 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. 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. 252-266.10.1504/IJISTA.2012.052503
  3. 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. 1-35.10.1016/j.asoc.2009.06.019
  4. 4. Sivanandam, S. N., S. N. Deepa. Introduction to Genetic Algorithms. Springer. ISBN 978-3-540-73189-4.
  5. 5. Zargar, G. R., T. Baghaie. Category Based Intrusion Detection Using PCA. – Journal of Information Security, Vol. 3, 2012, pp. 259-271.10.4236/jis.2012.34033
  6. 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. 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. 136-141.10.1145/1167253.1167288
  8. 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. 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. 10. Davis, J. J., A. J. Clark. Data Preprocessing for Anomaly Based Network Intrusion Detection. – Computer & Security, 2011, Elseiver, pp. 353-375.10.1016/j.cose.2011.05.008
  11. 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. 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. 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. 1-17.10.21608/asat.2009.23490
  14. 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. 15. Hashemi, V. M., Z. Muda, W. Yassin. Improving Intrusion Detection Using Genetic Algorithm. – Information Technology Journal, Vol. 12, 2013, No 11, pp. 2167-2173.10.3923/itj.2013.2167.2173
  16. 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. 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. 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. 25-29.10.9790/0661-0752529
  19. 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. 20. Singh, B. Network Security and Management. PHI Learning Pvt Ltd. Second Edition. 2009.
  21. 21. Soman, K. P., S. Diwakar, V. Ajay. Insight into Data Mining Theory and Practice. PHI Learning Pvt Ltd. Third Edition. 2008.
  22. 22. Dunham, M. H. Data Mining Introductory and Advanced Topics. Pearson Education, Seventeeth, 2013.
  23. 23. Rajesekaran, S., G. A. Vijayalaksmi Pai. Neural Networks, Fuzzy Logic and Genetic Algorithms Synthesis and Applications. PHI, India, 2010.
  24. 24. Sumathi, S., S. N. Sivanandam. Data Mining in Security, Studies in Computational Intelligence (SCI). Springer, 2006, pp. 629 -648,10.1007/978-3-540-34351-6_25
  25. 25. Janvier, 2013. http://eric.univlyon2.fr/~ricco/tanagra/fichiers/en_Tanagra_Nb_Components_PCA.pdf
  26. 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. 27. Eibe, F., I. H. Written. Generating Accurate Rulesets without Global Optimization. – In: Proc. of 15 International Conference on Machine Learning, 1998.
  28. 28. Rizwan, A., et al. Architecture of Hybrid Mobile Social Networks for Efficient Content Delivery. – Wireless Personal Communications, Vol. 80, 2015, No 1, pp. 85-96.10.1007/s11277-014-1996-4
  29. 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. 34-37.10.1002/dac.3437
  30. 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. 3031-3039.10.1109/ACCESS.2017.2786681
  31. 31. Ezhilarasi, M., V. Krishnaveni. A Survey on Wireless Sensor Network: Energy and Lifetime Perspective. – Taga Journal of Graphic Technology, Vol. 14, 2018.
  32. 32. Nagarajan, M., S. Karthikeyan. A New Approach to Increase the Life Time and Efficiency of Wireless Sensor Network. IEEE, 2012.10.1109/ICPRIME.2012.6208349
  33. 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.
DOI: https://doi.org/10.2478/cait-2018-0042 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702
Language: English
Page range: 111 - 119
Submitted on: Jan 22, 2018
Accepted on: Jul 30, 2018
Published on: Sep 19, 2018
Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2018 L. Gnanaprasanambikai, Nagarajan Munusamy, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.