Identify Features and Parameters to Devise an Accurate Intrusion Detection System Using Artificial Neural Network
The aim of this article is to explain how features of attacks could be extracted from the packets. It also explains how vectors could be built and then applied to the input of any analysis stage. For analyzing, the work deploys the Feedforward-Back propagation neural network to act as misuse intrusion detection system. It uses ten types if attacks as example for training and testing the neural network. It explains how the packets are analyzed to extract features. The work shows how selecting the right features, building correct vectors and how correct identification of the training methods with nodes- number in hidden layer of any neural network affecting the accuracy of system. In addition, the work shows how to get values of optimal weights and use them to initialize the Artificial Neural Network.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1333024Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1932
 Karen S. , Peter M., "Guide to Intrusion Detection and Prevention Systems (IDPS)", Recommendations of the National Institute of Standards and Technology, Special Publication 800-94, February 2007.
 John Mc., Alan Ch., and Julia A., "Defending Yourself: The Role of Intrusion Detection Systems", IEEE Software, volume 17, No. 5, 0740- 7459, September / October 2000.
 Rodrigo Rubira Brance, " KIDS-Kernel Intrusion Detection System", Hacker 2 Hacker Conference IV 2007 - Brazil, 11/09/2007.
 Bob R., "Hiding Intrusion Dectection System (IDS)", Whitepaper, in www.infosecwriters.com/text_resources/pdf/wp-003.pdf, found on 2010.
 Latifur Khan, Mamoun Awad, and Bhavani Thuraisingham, "A new intrusion detection system using support machines and hierarchical clustering" , The BLDB Journal, 1066-8888, Volume 16, No. 4, Octobor-2007, pp (507-521).
 Ajith Abraham, "Artificial Neural Network", Handbook of Measuring System Design, 0-470-02143-8, 2005.
 Klaus D., Alexander K., and Horst-Michael G., "Transfer Functions in Artificial Neural Network", http//:www.brains-minds-media.org, Accessed on 2010, 2005.
 Jake R., Meng-Jang Lin, andRisto Mi., "Intrusion Detection with Neural Networks", Advances in Neural Information Processing Systems 10,Cambridge,MA: MITPress,1998.
 Zhimin Yang, Xiumei Wei, Luyan Bi ,Dongping Shi ,Hui Li, "An Intrusion Detection System Based on RBF Neural Network", The 9th International Conference on Computer Supported Cooperative Work in Design Proceedings, 2005.
 Wang Jing-xin, Wang Zhi-ying, and Dai Kui, " A Network Intrusion Detection based on the Artificial Neural Network", ACM , 1-58113-955- 1, Vol. 85, Proceedings of the 3rd international conference on Information security, 2004.
 Allan Liska, " Network Security: Understanding Types of Attacks" http://www.informit.com/articles/article.aspx?p=31964, accessed on 2010.
 Simon H. and Ray Hunt, " A taxonomy of network and computer attacks" Computer and Security journal, 0167-4048, 2004.
 Kristopher Kendall, "A Database of Computer Attacks for the Evaluation of Intrusion Detection System", A thesis submitted to Department of Electrical Engineering and Computer Science At MASSACHUSETTS INSTITUTE OF TECHNOLOGY, 1999.
 Mansor Sh. and Amir Sh., " Fast Neural Intrusion Detection System Based on Hidden Weight optimization Algorithm and Feature Selection", World Applied Sciences Journal 7 (Special Issue of Computer & IT): 45-53, 2009
 Jimmy Sh. and Heidar A., "Network Intrusion Detection System Using Neural Networks", Fourth International Conference on Natural Computation, 978-0-7695-3304-9, 2008.
 Qinzhen Xu,, Wenjiang Pei, Luxi Yang, and Qiangfu Zhao, "An Intrusion Detection Approach Based on Understandable Neural Network Trees", JCSNS International Journal of Computer Science and Network Security, Vol.6 No.11, November 2006
 Vipin Kumar, Jaideep Srivastava and Aleksandar Lazarevic, " Intrusion Detection: Survey" Resource Secured Journal, Vol. 5, 10.1007/b104908v, 2005, pp (19-78).