In today’s world, computer network is evolving very rapidly. Most public
or/and private companies set up their own local networks system for the
purpose of promoting communication and data sharing within the companies.
Unfortunately, their data and local networks system are under risks.
With the advanced computer networks, the unauthorized users attempt to
access their local networks system so as to compromise the integrity, confidentiality
and availability of resources. Multiple methods and approaches
have to be applied to protect their data and local networks system against malicious
attacks. The main aim of our paper is to provide an intrusion detection
system based on soft computing algorithms such as Self Organizing Feature
Map Artificial Neural Network and Genetic Algorithm to network intrusion
detection system. KDD Cup 99 and 1998 DARPA dataset were employed
for training and testing the intrusion detection rules. However, GA’s traditional
Fitness Function was improved in order to evaluate the efficiency and
effectiveness of the algorithm in classifying network attacks from KDD Cup
99 and 1998 DARPA dataset. SOFM ANN and GA training parameters were
discussed and implemented for performance evaluation. The experimental
results demonstrated that SOFM ANN achieved better performance than GA,
where in SOFM ANN high attack detection rate is 99.98%, 99.89%, 100%,
100%, 100% and low false positive rate is 0.01%, 0.1%, 0%, 0%, 0% for DoS,
R2L, Probe, U2R attacks, and Normal traffic respectively.
References
[1]
Ibrahim, L.M., Basheer, D.T. and Mahmod, M.S. (2013) A Comparison Study for Intrusion Database (Kdd99, Nsl-Kdd) Based on Self Organization Map (SOM) Artificial Neural Network. Journal of Engineering Science and Technology, 8, 107-119.
[2]
Sharma, G.D. and Kumar, V. (2016) Towards Configured Intrusion Detection Systems. Global Journals Inc., USA, Vol. 16, Version 1.0.
[3]
Bunel, P. (2004) An Introduction to Intrusion Detection Systems. SANS Institute, GIAC Security Essentials, Certificate (GSEC), Practical Assignment, Version 1.4c, SANS Conference, London.
[4]
Bankovi, Z., Stepanovi, D., Bojani, S. and Nieto-Taladriz, O. (2007) Improving Network Security Using Genetic Algorithm Approach. Computers and Electrical Engineering, 33, 438-451. https://doi.org/10.1016/j.compeleceng.2007.05.010
[5]
Ramadas, M., Ostermann, S. and Tjaden, B. (2003) Detecting Anomalous Network Traffic with Self-Organizing Maps. In: Vigna, G., Kruegel, C. and Jonsson, E., Eds., Recent Advances in Intrusion Detection. RAID 2003. Lecture Notes in Computer Science, Vol. 2820, Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45248-5_3
[6]
Abdullah, B., Abd-Alghafar, I., Salama, G.I. and Abd-Alhafez, A. (2009) Performance Evaluation of a Genetic Algorithm Based Approach to Network Intrusion Detection System. 13th International Conference on Aerospace Sciences and Aviation Technology, ASAT-13, Military Technical College, Cairo, Egypt, 26-28 May 2009, 1-17.
[7]
Bridges, S.M. and Vaughn, R.B. (2000) Fuzzy Data Mining And Genetic Algorithms Applied to Intrusion Detection. Proceedings of 12th Annual Canadian Information Technology Security Symposium, Ottawa, Canada, 19-23 June 2000, 109-122.
[8]
Chittur, A. (2001) Model Generation for an Intrusion Detection System Using Genetic Algorithms. High School Honors Thesis, Ossining High School. In cooperation with Columbia Univ.
[9]
Crosbie, M. and Spafford, G. (1995) Applying Genetic Programming to Intrusion Detection. In Working Notes for the AAAI Symposium on Genetic Programming, MIT Press, Cambridge, MA, 1-8.
[10]
Li, W. (2004) A Genetic Algorithm Approach to Network Intrusion Detection. SANS Institute, USA, Vol. 15, 209-216.
[11]
Oksuz, A. (2007) Unsupervised Intrusion Detection System. Master’s Thesis, Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark.
[12]
Choksi, K., Shah, B. and Kale, O. (2014) Intrusion Detection System Using Self Organizing Map: A Survey. International Journal of Engineering Research and Applications, 4, 11-16.
[13]
Patole, V.A., Pachghare, V.K. and Kulkarni, P. (2010) Self Organizing Maps to Build Intrusion Detection Systems. Journal of Computer Applications, 1.
[14]
De La Hoz Franco, E., Ortiz Garcia, A., Ortega Lopera, J., De La Hoz Correa, E. and Mendoza Palechor, F. (2015) Implementation of an Intrusion Detection System Based on Self Organizing Map. Journal of Theoretical and Applied Information Technology, 71.
[15]
Sayad, S. (2010) Self-Organizing Maps (SOM). Bahen Centre Information Technology, Toronto, Ontario, Canada.
[16]
Jadhav, M.L. and Gaikwad, C.M. (2014) Implementation of Intrusion Detection System Using GA. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering (IJIREEICE), 2, 1733-1736.
[17]
Wong, M.L. and Leung, K.S. (2000) Data Mining Using Grammar Based Genetic Programming and Applications. Kluwer Academic Publishers, Dordrecht.
[18]
Paliwal, S. and Gupta, R. (2012) Denial-of-Service, Probing and Remote to User (R2L) Attack Detection Using Genetic Algorithm. International Journal of Computer Applications, 60, 57-62.
[19]
Sunita, S., Chandrakanta, B.J. and Chinmayee, R. (2016) A Hybrid Approach of Intrusion Detection Using ANN and FCM. European Journal of Advances in Engineering and Technology, 3, 6-14.
[20]
Jiang, X., Liu, K., Yan, J. and Chen, W. (2012) Application of Improved SOM Neural Network in Anomaly Detection. Physics Procedia, 33, 1093-1099. https://doi.org/10.1016/j.phpro.2012.05.179
[21]
Palomo, E.J., Domnguez, E., Luque, R.M. and Muoz, J. (2009) An Intrusion Detection System Based on Hierarchical Self-Organization. In: Proceedings of the International Workshop on Computational Intelligence in Security for Information Systems, Springer, Berlin, Heidelberg, 139-146. https://doi.org/10.1007/978-3-540-88181-0_18
[22]
Bahrololum, M., Salahi, E. and Khaleghi, M. (2009) Anomaly Intrusion Detection Design Using Hybrid of Unsupervised and Supervised Neural Network. International Journal of Computer Networks and Communications, 1, 26-33.
[23]
Vokorokos, L., Balaz, A. and Chovanec, M. (2006) Intrusion Detection System Using Self Organizing Map. Acta Electrotechnica et Informatica, 6, 1-6.
[24]
Gupta, N., Pareek, N. and Pandey, K. (2011) Genetic Algorithm Based Network Intrusion Detection System. International Journal of Advanced Research in Computer Science, 2, 480-483.
[25]
Proti, D.D. (2018) Review of KDD Cup’99, NSL-KDD and Kyoto 2006+ Datasets. Vojnotehniki Glasnik, 66, 580-596. https://doi.org/10.5937/vojtehg66-16670
[26]
Alzahrani, S. and Hong, L. (2018) Generation of DDoS Attack Dataset for Effective IDS Development and Evaluation. Journal of Information Security, 9, 225-241. https://doi.org/10.4236/jis.2018.94016
[27]
Amini, M., Jalili, R. and Shahriari, H.R. (2006) RT-UNNID: A Practical Solution to Real-Time Network-Based Intrusion Detection Using Unsupervised Neural Networks. Computers and Security, 25, 459-468. https://doi.org/10.1016/j.cose.2006.05.003
[28]
Lavender, B.E. (2010) Implementation of Genetic Algorithms into a Network Intrusion Detection System (netGA), and Integration into NProbe. Thesis Work, California State University, Sacramento.
[29]
Gong, R.H., Zulkernine, M. and Abolmaesumi, P. (2005) A Software Implementation of a Genetic Algorithm Based Approach to Network Intrusion Detection. Proceedings of the 6th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and 1st ACIS International Workshop on Self-Assembling Wireless Networks, May 2005, 246–253.
[30]
Lu, W. and Traore, I. (2004) Detecting New Forms of Network Intrusion Using Genetic Programming. Computational Intelligence, 20, 475-494. https://doi.org/10.1111/j.0824-7935.2004.00247.x
