The week's pick
Random Articles
Reseach Article
Anomaly Detection based on Machine Learning: Dimensionality Reduction using PCA and Classification using SVM
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 47 - Number 21 |
| Year of Publication: 2012 |
| Authors: Annie George |
10.5120/7470-0475
|
Annie George . Anomaly Detection based on Machine Learning: Dimensionality Reduction using PCA and Classification using SVM. International Journal of Computer Applications. 47, 21 ( June 2012), 5-8. DOI=10.5120/7470-0475
Abstract
Anomaly detection has emerged as an important technique in many application areas mainly for network security. Anomaly detection based on machine learning algorithms considered as the classification problem on the network data has been presented here. Dimensionality reduction and classification algorithms are explored and evaluated using KDD99 dataset for network IDS. Principal Component Analysis for dimensionality reduction and Support Vector Machine for classification have been considered for the application on network data and the results are analysed. The result shows the decrease in execution time for the classification as we reduce the dimension of the input data and also the precision and recall parameter values of the classification algorithm shows that the SVM with PCA method is more accurate as the number of misclassification decreases.
References
- M. M. Sebring, E. Shellhouse, M. E. Hanna, and R. Alan Whitehurst, "Expert systems in intrusion detection: A case study", In Proceedings of the 11th National Comput Security Conference, Baltimore, Maryland.
- W. K. Lee, S. J. Stolfo, "A data mining framework for building intrusion detection model", In: Gong L. , Reiter M. K. (eds. ): Proceedings of the IEEE Symposium on Security and Privacy. Oakland, CA: IEEE Computer Society Press, 1999.
- W. K. Lee, et al. , "Mining audit data to build intrusion detection models", In Proc. Int. Conf. Knowledge Discovery and Data Mining (KDD'98), pp. 66-72, 1998.
- H. Güne? Kayac?k, A. NurZincir-Heywood, Malcolm I. Heywood, "Selecting Features for Intrusion Detection: A Feature Relevance Analysis on KDD 99 Intrusion Detection Datasets", Dalhousie University, Faculty of Computer Science, 6050 University Avenue, Halifax, Nova Scotia.
- Mahbod Tavallaee,Ebrahim Bagheri, Wei Lu, and Ali A. Ghorbani, "A Detailed Analysis of the KDD CUP 99 Data Set ", Proceedings of the IEEE Symposium on Computational Intelligence in Security and Security Applications, 2009.
- Fengxi Song, Zhongwei Guo, Dayong Mei, "Feature selection using principal component analysis", Department of Automation and Simulation New Star Research Inst. Of Applied Tech. in Hefei City Hefei, China, International Conference on System Science, Engineering Design and Manufacturing Informatization, 2010.
- Lindsay I Smith, "A tutorial on Principal Components Analysis".
- CHEN Bo, Ma Wu, "Research of Intrusion Detection based on Principal Components Analysis", Information Engineering Institute, Dalian University, China, Second International Conference on Information and Computing Science, 2009.
- Chin-Jen Lin, "Formulations of Support Vector Machines: A Note from an Optimization Point of View", Department of Computer Science and Information Engineering, National Taiwan University, Neural Computation 13, 2001.
- Zhangxue-qin, Gu chun-hua and Linjia-jun, "Intrusion detection system based on feature selection and support vector machine", East China University of Science and Technology, Proceedings of IEEE, 2006.
Index Terms
Keywords