The week's pick
Reseach Article
Artificial Neural Network based Intrusion Detection System: A Survey
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 39 - Number 6 |
| Year of Publication: 2012 |
| Authors: Bhavin Shah, Bhushan H Trivedi |
10.5120/4823-7074
|
Bhavin Shah, Bhushan H Trivedi . Artificial Neural Network based Intrusion Detection System: A Survey. International Journal of Computer Applications. 39, 6 ( February 2012), 13-18. DOI=10.5120/4823-7074
Abstract
Detecting unknown or modified attacks is one of the recent challenges in the field of IDS. Anomaly based IDS can play a very important role in this case. In the first part of this paper, we will focus on how ANN is recently used to address these issues. Number of the researchers has already shown the importance of the various Artificial Neural Network (ANN) based techniques for anomaly detection. In this paper, we will focus on Simple and Hybrid ANN based approach for anomaly detection. In simple approach we will discuss on how Back Propagation Neural Network (BPNN), Self Organizing Maps (SOM), Support Vector Machine (SVM), and Simulated Annealing Neural Network (SA) are used for anomaly detection? While in hybrid approach, we will focus on how more than one above technique are used? In the second part of the paper, we will try to compare the different ANN based techniques in terms of training time, number of the epochs required, converge rate, detection rate, learning approach, etc. Finally we will provide guidelines for the future work.
References
- V. K. Pachghare, Parag Kulkarni, Deven M. Nikam , 2009, Intrusion Detection System Using Self Organizing Maps, IEEE.
- Zhang Wei, Wang Hao-yu, 2010, Intrusive Detection Systems Design based on BP Neural Network, IEEE.
- Paulo M. Mafra, Vinicius Moll, Joni da Silva Fraga, 2010, Octopus-IIDS: An Anomaly Based Intelligent Intrusion Detection System, IEEE.
- Song Guangjun, Zhang Jialin, Sun Zhenlong, 2008, The Research of Dynamic Change Learning Rate Strategy in BP Neural Network and Application in Network Intrusion Detection, IEEE, and also in 3rd International Conference on Innovative Computing Information and Control (ICICIC'08).
- Meijuan Gao, Jingwen Tian, 2009, Network Intrusion Detection Method Based on Improved Simulated Annealing Neural Network, IEEE and also at International Conference on Measuring Technology and Mechatronics Automation.
- Jing Bi, Kun Zhang, Xiaojing Cheng , 2009, Intrusion Detection Based on RBF Neural Network, IEEE and also at International Symposium on Information Engineering and Electronic Commerce.
- Srinivas Mukkamala, Guadalupe Janoski, Andrew Sung, 2002, Intrusion Detection: Support Vector Machines and Neural Networks, IEEE and Proceedings of the 2002 International Joint Conference on Neural Networks IJCNN02 Cat No 02 CH3 7290.
- X. Haijun, P. Fang, W. Ling, and L. Hongwei, 2007, Ad hoc-based feature selection and support vector machine, IEEE and also at Grey Systems and Intelligent Services 2007 GSIS 07.
- Yingbing Yu, Anomaly Detection of Masqueraders Based Upon Typing Biometrics And Probabilistic Neural Network, , ACM and also at Journal of Computing Sciences in Colleges, Volume 25 Issue 5.
- Milan Tuba, Dusan Bulatovic, 2010, Design of an Intrusion Detection System Based on Bayesian Networks, ACM.
- Nabeel Younus Khan, Bilal Rauf, Kabeer Ahmed, 2010, Comparative Study of Intrusion Detection System and its Recovery mechanism, IEEE.
- Ondrej Linda, Todd Vollmer, Milos Manic, 2009, Neural Network Based Intrusion Detection System for Critical Infrastructures, IEEE and also at Proceedings of International Joint Conference on Neural Networks.
- P. Anderson, Computer security threat monitoring and surveillance, Technical report, James P. Anderson Co, 1980.
- D. E. Denning, 1987, An Intrusion Detection Model, IEEE Transactions on Software Engineering, Vol. SE-13, February 1987, pp. 222-232.
- KDD Cup 1999 Data [EB/DL], 1999, University of California, Irvine. http://kdd.rcs.uci.edu/databases/kddcup99/kddcup99.htm
- DARPA Intrusion Detection Evaluation Data Sets, 2002, MIT Lincoln Laboratory. http://www.ll.mit.edu/IST/ideval/data/data_index.html
- Matthew V. Mahoney and Philip K. Chan , 2003, An Analysis of the 1999 DARPA/Lincoln Laboratory Evaluation Data for Network Anomaly Detection, Proceedings of the Sixth International Symposium on Recent Advances in Intrusion Detection, Springer.
- Mahbod Tavallaee, Ebrahim Bagheri, Wei Lu, and Ali A. Ghorbani, 2009, A Detailed Analysis of the KDD CUP 99 Data Set, Second IEEE Symposium on Computational Intelligence for Securityand Defense Applications (CISDA) 2009.
- J. McHugh, 2000 , Testing intrusion detection systems: a critique of the 1998 and 1999 DAPRA intrusion detection system evaluations as performed by lincoln laboratory, ACM Transactions on Information and System Security, vol. 3, no. 4, pp. 262–294, 2000.
- NSL- KDD Dataset, Faculty of Computer Science University of New Brunswick, http://www.iscx.ca/NSL-KDD.
Index Terms
Keywords