The week's pick
Reseach Article
Efficient Classifier for R2L and U2R Attacks
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 45 - Number 21 |
| Year of Publication: 2012 |
| Authors: P. Gifty Jeya, M. Ravichandran, C. S. Ravichandran |
10.5120/7076-9751
|
P. Gifty Jeya, M. Ravichandran, C. S. Ravichandran . Efficient Classifier for R2L and U2R Attacks. International Journal of Computer Applications. 45, 21 ( May 2012), 29-32. DOI=10.5120/7076-9751
Abstract
Intrusion Detection System (IDS) is an effective security tool that helps to prevent unauthorized access to network resources by analysing the network traffic and classifying the records as either normal or anomalous. In this paper, a new classification method using Fisher Linear Discriminant Analysis (FLDA) is proposed. The features of KDD Cup '99 attack dataset are reduced for each class of attacks using correlation based feature selection method. Then with the reduced feature set, discriminant analysis is done for the classification of records. Comparison with other approaches reveals that our approach achieves good classification rate for R2L (Remote-to-Local) and U2R (User-to-Root) attacks.
References
- Adel Jahanbani and Hossein Karimi, "A new approach for detecting intrusions based on the PCA neural networks", Journal of Basic and Applied Scientific Reasearch, pp. 672-679, 2012.
- R. Agarwal and M. V. Joshi, "PNrule: A new framework for learning classifier models in data mining (A case study in Network Intrusion Detection), IBM research division technical report no. RC-21719, 2000.
- Beghdad. R, "Training all the KDD dataset to classify and detect attacks" in International Journal on Neural and Mass – Parallel computing and Information Systems, Vol. 17, March 2007.
- Y. W. Chen, "Traffic behaviour analysis and modelling of sub-networks", in International journal of network management, Vol. 12, pp. 323-330, September 2002.
- H. Gunes Kayacik, A. Nur Zincir Heywood and I. Heywood, "On the capability of an SOM based Intrusion Detection System", in Proceedings of the International conference on Neural Networks, Vol. 3, pp. 1808-1813.
- H. Gunes Kayacik, A. Nur Zincir Heywood and I. Heywood, "An hierarchical SOM-based intrusion detection" in Journal on Engineering Applications of Artificial Intelligence, Vol. 20, pp. 439-451, 2007.
- KDD Cup 1999 Intrusion Detection Dataset. [Online]. Available:http://kdd. ics. uci. edu/databases/kddcup99/kddcup99. html.
- S. S. Kim and A. L. N. Reddy, "Statistical techniques for detecting traffic anomalies through packet header data", IEEE/ACM Transaction on Networking, Vol. 16, no. 3, pp. 562-575, January 2008.
- H. Moayedi and M. Masnadi-Shirazi, "Arima model for network traffic prediction and anomaly detection", in Proc. ITSim 2008, pp. 1-6, August 2008.
- V. Paxson, "Bro: A system for detecting network intruders in realtime", in Proc. USENIX Security Symposium, January 1998.
- M. Roesch, "Snort-lightweight intrusion detection for networks" in Proc. USENIX LISA 1999, pp. 229-238, November 1999.
- R. Sekar, M. Bendre, D. Dhurjati and P. Bollineni, "A fast automation based method for detecting anomalous program behaviors", in Proc. IEEE Symposium on Security and Privacy, May 2001.
- R. Shanmugavadivu and Dr. N. Nagarajan, " Network intrusion detection system using fuzzy logic", in Indian Journal of Computer Science and Engineering, Vol. 2, pp. 101-111.
- SPSS Inc. , "SPSS 13. 0 Base User's Guide"
- Theuns Verwoerd, Ray Hunt, "Intrusion detection techniques and approaches", Computer Communications, Vol. 25, pp. 1356-1365, 2002.
- Weka tool. [Online]. Available: http://www. cs. waikato. ac. nz/ml/weka.
- Y. Zhang, Z. Ge, A. Greenberg and M. Roughan, "Network Anomography", in Proc. USENIX IMC 2005, pp. 317-330, October 2005.
Index Terms
Keywords