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.
[1] 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.
[2] 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.
[3] Rodrigo Rubira Brance, " KIDS-Kernel Intrusion Detection System",
Hacker 2 Hacker Conference IV 2007 - Brazil, 11/09/2007.
[4] Bob R., "Hiding Intrusion Dectection System (IDS)", Whitepaper, in
www.infosecwriters.com/text_resources/pdf/wp-003.pdf, found on 2010.
[5] 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).
[6] Ajith Abraham, "Artificial Neural Network", Handbook of Measuring
System Design, 0-470-02143-8, 2005.
[7] Klaus D., Alexander K., and Horst-Michael G., "Transfer Functions in
Artificial Neural Network", http//:www.brains-minds-media.org,
Accessed on 2010, 2005.
[8] Jake R., Meng-Jang Lin, andRisto Mi., "Intrusion Detection with Neural
Networks", Advances in Neural Information Processing Systems
10,Cambridge,MA: MITPress,1998.
[9] 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.
[10] 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.
[11] Allan Liska, " Network Security: Understanding Types of Attacks"
http://www.informit.com/articles/article.aspx?p=31964, accessed on
2010.
[12] Simon H. and Ray Hunt, " A taxonomy of network and computer
attacks" Computer and Security journal, 0167-4048, 2004.
[13] 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.
[14] 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
[15] Jimmy Sh. and Heidar A., "Network Intrusion Detection System Using
Neural Networks", Fourth International Conference on Natural
Computation, 978-0-7695-3304-9, 2008.
[16] 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
[17] Vipin Kumar, Jaideep Srivastava and Aleksandar Lazarevic, " Intrusion
Detection: Survey" Resource Secured Journal, Vol. 5,
10.1007/b104908v, 2005, pp (19-78).
[1] 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.
[2] 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.
[3] Rodrigo Rubira Brance, " KIDS-Kernel Intrusion Detection System",
Hacker 2 Hacker Conference IV 2007 - Brazil, 11/09/2007.
[4] Bob R., "Hiding Intrusion Dectection System (IDS)", Whitepaper, in
www.infosecwriters.com/text_resources/pdf/wp-003.pdf, found on 2010.
[5] 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).
[6] Ajith Abraham, "Artificial Neural Network", Handbook of Measuring
System Design, 0-470-02143-8, 2005.
[7] Klaus D., Alexander K., and Horst-Michael G., "Transfer Functions in
Artificial Neural Network", http//:www.brains-minds-media.org,
Accessed on 2010, 2005.
[8] Jake R., Meng-Jang Lin, andRisto Mi., "Intrusion Detection with Neural
Networks", Advances in Neural Information Processing Systems
10,Cambridge,MA: MITPress,1998.
[9] 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.
[10] 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.
[11] Allan Liska, " Network Security: Understanding Types of Attacks"
http://www.informit.com/articles/article.aspx?p=31964, accessed on
2010.
[12] Simon H. and Ray Hunt, " A taxonomy of network and computer
attacks" Computer and Security journal, 0167-4048, 2004.
[13] 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.
[14] 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
[15] Jimmy Sh. and Heidar A., "Network Intrusion Detection System Using
Neural Networks", Fourth International Conference on Natural
Computation, 978-0-7695-3304-9, 2008.
[16] 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
[17] Vipin Kumar, Jaideep Srivastava and Aleksandar Lazarevic, " Intrusion
Detection: Survey" Resource Secured Journal, Vol. 5,
10.1007/b104908v, 2005, pp (19-78).
@article{"International Journal of Information, Control and Computer Sciences:56095", author = "Saman M. Abdulla and Najla B. Al-Dabagh and Omar Zakaria", title = "Identify Features and Parameters to Devise an Accurate Intrusion Detection System Using Artificial Neural Network", abstract = "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.
", keywords = "Artificial Neural Network, Attack Features, MisuseIntrusion Detection System, Training Parameters.", volume = "4", number = "10", pages = "1530-5", }
