1. bookVolume 18 (2018): Edition 3 (September 2018)
Détails du magazine
License
Format
Magazine
eISSN
1314-4081
Première parution
13 Mar 2012
Périodicité
4 fois par an
Langues
Anglais
Accès libre

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

Publié en ligne: 19 Sep 2018
Volume & Edition: Volume 18 (2018) - Edition 3 (September 2018)
Pages: 111 - 119
Reçu: 22 Jan 2018
Accepté: 30 Jul 2018
Détails du magazine
License
Format
Magazine
eISSN
1314-4081
Première parution
13 Mar 2012
Périodicité
4 fois par an
Langues
Anglais

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.Search in Google Scholar

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.2012.052503Search in Google Scholar

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.2009.06.019Search in Google Scholar

4. Sivanandam, S. N., S. N. Deepa. Introduction to Genetic Algorithms. Springer. ISBN 978-3-540-73189-4.Search in Google Scholar

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.2012.34033Search in Google Scholar

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.Search in Google Scholar

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. 136-141.10.1145/1167253.1167288Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.2011.05.008Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.2009.23490Search in Google Scholar

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.Search in Google Scholar

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.2013.2167.2173Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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. 25-29.10.9790/0661-0752529Search in Google Scholar

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.Search in Google Scholar

20. Singh, B. Network Security and Management. PHI Learning Pvt Ltd. Second Edition. 2009.Search in Google Scholar

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

22. Dunham, M. H. Data Mining Introductory and Advanced Topics. Pearson Education, Seventeeth, 2013.Search in Google Scholar

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

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_25Search in Google Scholar

25. Janvier, 2013. http://eric.univlyon2.fr/~ricco/tanagra/fichiers/en_Tanagra_Nb_Components_PCA.pdfSearch in Google Scholar

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.Search in Google Scholar

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

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/s11277-014-1996-4Search in Google Scholar

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.3437Search in Google Scholar

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.2017.2786681Search in Google Scholar

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

32. Nagarajan, M., S. Karthikeyan. A New Approach to Increase the Life Time and Efficiency of Wireless Sensor Network. IEEE, 2012.10.1109/ICPRIME.2012.6208349Search in Google Scholar

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.Search in Google Scholar

Articles recommandés par Trend MD

Planifiez votre conférence à distance avec Sciendo