CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Neural Network based Intrusion Detection Systems

by Sodiya A.S, Ojesanmi O.A, Akinola O.C., Aborisade O
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 106 - Number 18
Year of Publication: 2014
Authors: Sodiya A.S, Ojesanmi O.A, Akinola O.C., Aborisade O
10.5120/18705-9636

Sodiya A.S, Ojesanmi O.A, Akinola O.C., Aborisade O . Neural Network based Intrusion Detection Systems. International Journal of Computer Applications. 106, 18 ( November 2014), 19-24. DOI=10.5120/18705-9636

@article{ 10.5120/18705-9636,
author = { Sodiya A.S, Ojesanmi O.A, Akinola O.C., Aborisade O },
title = { Neural Network based Intrusion Detection Systems },
journal = { International Journal of Computer Applications },
issue_date = { November 2014 },
volume = { 106 },
number = { 18 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = { 19-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume106/number18/18705-9636/ },
doi = { 10.5120/18705-9636 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:39:45.107740+05:30
%A Sodiya A.S
%A Ojesanmi O.A
%A Akinola O.C.
%A Aborisade O
%T Neural Network based Intrusion Detection Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 106
%N 18
%P 19-24
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recent Intrusion Detection Systems (IDSs) which are used to monitor real-time attacks on computer and network systems are still faced with problems of low detection rate, high false positive, high false negative and alert flooding. This paper present a Neural Network-based approach that combined supervised and unsupervised learning techniques designed to correct some of these problems. The design is divided into two phases namely: Training and Detection. In the training phase, Multiple Self–Organizing Map algorithm (SOM) was constructed to capture a number of different input patterns, discover significant features in these patterns and learn how to classify input. Sigmoid Activation Function (SAF) was used to transform the input into a reasonable value (0, 1). The learning weights were randomly assigned in the range (-1, +1) to obtain the output consistent with the training. SAF was represented using a hyperbolic tangent in order to increase the learning speed and make learning efficient. Momentum and adaptive learning rates were introduced to significantly improve the performance of the back-propagation neural network. The trained lattice of neuron was used as input in the back propagation for the real-time monitoring and detection of intrusive activities. The design was implemented in Visual Basic. Net. An evaluation was carried out using Network Traffic data collected from Defence Advanced Research Projects Agency dataset consisting of normal and intrusive traffic. The training model was performed by means of Root Mean Square (RMS) error analysis using learning rate of 0. 70, 4 input layers, 8 hidden layers and 2 output layers. The evaluation result of the new design showed a promising and improved technique when compared with the recent and best known related work.

References
  1. Alsharafat W. (2013), "Applying Artificial Neural Network and eXtended Classifier System for Network Intrusion Detection", The International Arab Journal of Information Technology, Vol. 10, No. 3, pp. 230-238.
  2. Bhavin S. and Bhushan H. T. (2012), "Artificial Neural Network based Intrusion Detection System: A Survey", International Journal of Computer Applications 39(6):13-18.
  3. Chaivat Jirapummin, Naruemon, Wattanapongsakorn and Prasert Kanthamanon (2000), "Hybrid Neural Networks for Intrusion Detection System", Department of Computer Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangkok , Thailand.
  4. Devikrishna K. S. and Ramakrishna B. B. (2013), An Artificial Neural Network based Intrusion Detection System and Classification of Attacks, International Journal of Engineering Research and Applications (IJERA) ,Vol. 3, Issue 4, pp. 1959-1964 1959, ISSN: 2248-9622.
  5. Girardin L, "An eye for Network Intruder-Administator Shootouts," Proc. of the 1st USENIX Workshop on Intrusion Detection and Network Monitoring, 1999 Comparison of Supervised Neural Network in Intrusion Detection. http://ailab. das. ucdavis. edu/papers/vu02nn. pdf
  6. Goh Kia Eng, and Abdul Manan Ahmad (2005) " Malay Speech Recognition using Self-Organizing Map and Multilayer Perceptron", Proceedings of the Postgraduate Annual Research Seminar, pp. 233-237.
  7. John Zhong, Lei and Ali Ghorbani (2004) "Network Intrusion Detection Using an Improved Competitive Learning Neural Network" in Proceedings of the 2nd Annual Conference on Communication Networks and Services Research (CNSR 2004), Canada. IEEE Computer Society, ISBN 0-7695-2096-0 pp. 190-197.
  8. Jun Li, Gerhard Eschelbeck , "Multi-Tiered Intrusion Detection System" http://fmg-www. cs. ucla. edu/ficus-members/lijun/pubs/TR010027. pdf
  9. Kaleton Internet (2002)"Combination of Misuse and Anomaly. Network Intrusion Detection Systems", March 2002. Kaleton Internet. Dept. 5364 Suite 145. 269/2 Soi Potisarn Moo 6. Naklua Banglamung. Chonburi 20150. Thailand. http://www. kaleton. com/research/kaletonidspaper. pdf
  10. Kohonen. T (2001. ) "Self-Organizing Maps", 3rd extended ed, ser. Information Sciences, Berlin, Germany: Springer, vol. 30
  11. Lee W , Stolfo S, and Mok K (1999) "A data mining framework for building intrusion detection models", In Proceedings of the IEEE Symposium on Security and Privacy, Oakland, California.
  12. Lee W , Stolfo S, and Mok K (2002) The Third International Discovery and Data Mining Tools Competition. [online], http://kdd. ics. uci. edu/database/kddCup99/kddCup99. html
  13. Mohammad S. A. and Abu N. B. (2012)," An Implementation of Intrusion Detection System using Genetic Algorithm ", International Journal of Network Security & Its Applications (IJNSA), Vol. 4, No. 2, March 2012.
  14. Mostaque M. H. (2013), Current Studies on Intrusion Detection System, Genetic Algorithm and Fuzzy Logic, International Journal of Distributed and Parallel Systems (IJDPS) Vol. 4, No. 2, pp. 35-47.
  15. Nadiammai G. V. ,Hemalatha M. (2013) , " Handling Intrusion Detection System using Snort Based Statistical Algorithm and Semi-supervised Approach "Research Journal of Applied Sciences, Engineering and Technology 6(16): 2914-2922, ISSN: 2040-7459
  16. Northcut S and Novak J, "Network Intrusion Detection", 3rd ed. Indianapolis, IN: New Riders Publishing, 2002.
  17. Parveen K. and Nitin G. (2014), A Hybrid Intrusion Detection System Using Genetic-Neural Network, International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 , pp. 59-63.
  18. Peter Lichodzijewski, A. Nur Zincir, Heywood (2003) "Dynamic Intrusion Detection using Self-Organizing Maps", Faculty of Comp. Science Dalhousie University Halifax, NS http://cs. stmarys. ca/~jmac/482-2005/CITSS-2k2. pdf
  19. Pingchuan Ma (2003), "Log Analysis-Based Intrusion Detection via Unsupervised Learning", Master of Science, School of Informatics, University of Edinburgh Steve Lawrence
  20. Prasanta Gogoi1, D. K. Bhattacharyya1, B. Borah1 and Jugal K. Kalita. (2013), "MLH-IDS: A Multi-Level Hybrid Intrusion Detection Method", The Computer Journal, 57(4), pp. 602-623
  21. Reddy E. K. (2013), Neural Networks for Intrusion Detection and Its Applications, Proceedings of the World Congress on Engineering 2013 Vol II, WCE 2013, July 3 - 5, London, U. K.
  22. Robert Birkely. (June 2003) " A Neural Network Based Intelligent Intrusion System "http://www. rbirkely. com/cv/intelligent_intrusion_detection_system. pdf
  23. Sarasamma S, Zhu Q, Huff J, "Hierarchical Kohonen Net for Anomaly Detection in Network Security", IEEE Transactions on Systems, Man and Cybernetics - part B, vol. 35, No. 2, 2005.
  24. Shah A. T. , Jagtap S. S. , Kakade P. P. , Tekawade N. B. , and Daflapurkar P. M. (2014), "A Real-Time Intrusion Detection System using Artificial Neural Networks (ANN)", International Journal of Emerging Technology and Advanced Engineering, Vol. 4, Issue 3, pp. 756-759. (ISSN 2250-2459)
  25. Sherif M. B. (2013), "Implementation of Intelligent Multi-Layer Intrusion Detection Systems (IMLIDS)", International Journal of Computer Applications, 61(4):41-49.
  26. Sodiya A. S, Longe H. O. D and Akinwale A. T. (2004). "A new two tiered strategy to intrusion Detection", Emerald Information Management and Computer Security. vol. 13, No 5 http://www. emeraldinsight. com/09685227. htm
  27. Timo H. (2003) "Intrusion Detection with Neural Networks { Combination of Self-Organizing Maps and Radial Basis Function Networks for Human Expert Integration}" . http://www. ieee-nns. org/files/EAC_Research_2003_Report_Horeis. pdf
  28. Va N. P. Dao, Rao Vemuri (2002)" A Performance Comparison of Different Back Propagation Neural Networks Methods in Computer Network" http://www. cs. ucdavis. edu/~vemuri/papers/bp-intrusion%20detection. pdf
  29. Vu Dao (2002) "Computer Network Intrusion Detection Via Neural Networks Method"
Index Terms

Computer Science
Information Sciences

Keywords

Intrusion Detection Attack Neural network Security

Powered by PhDFocusTM