A New FLAME Selection Method for Intrusion Detection (FLAME-ID)
- 1 Al al-Bayt University, Mafraq, Jordan
Abstract
Due to the ever growing number of cyber attacks, especially of the online systems, development and operation of adaptive Intrusion Detection Systems (IDSs) is badly needed so as to protect these systems. It remains as a goal of paramount importance to achieve and a serious challenge to address. Different selection methods have been developed and implemented in Genetic Algorithms (GAs) to enhance the rate of detection of the IDSs. In this respect, the present study employed the eXtended Classifier System (XCS) for detection of intrusions by matching the incoming environmental message (packet) with a classifiers pool to determine whether the incoming message is a normal request or an intrusion. Fuzzy Clustering by Local Approximation Membership (FLAME) represents the new selection method used in GAs. In this study, Genetic Algorithm with FLAME selection (FGA) was used as a production engine for the XCS. For comparison purposes, different selection methods were compared with FLAME selection and all experiments and evaluations were performed by using the KDD’99 dataset.
- Surat, S., Werasak, K., Witcha, Ch. and Siriporn, Ch. (2005) Network Anomaly Detection Using Soft Computing. Proceedings of World Academy of Science, Engineering and Technology, 9, 140-144.
- Khan, M.S.A. (2011) Rule Based Network Intrusion Detection Using Genetic Algorithm. International Journal of Computer Applications, 18, 26-29.
- Srinivasu, P. and Avadhani, P.S. (2012) Genetic Algorithm Based Weight Extraction Algorithm for Artificial Neural Network Classifier in Intrusion Detection. Procedia Engineering, 38, 144-153. https://doi.org/10.1016/j.proeng.2012.06.021
- Li, W.S., Bai, X.M., Duan, L.Z. and Zhang, X. (2011) Intrusion Detection Based on Ant Colony Algorithm of Fuzzy Clustering. International Conference on Computer Science and Network Technology, IEEE, Piscataway, 1642-1645.
- Geramiraz, F., Memaripour, A.S. and Abbaspour, M. (2012) Adaptive Anomaly-Based Intrusion Detection System Using Fuzzy Controller. International Journal of Network Security, 14, 352-361.
- Ganapathy, S., Yogesh, P. and Kannan, A. (2012) Intelligent Agent Based Intrusion Detection Using Enhanced Multiclass SVM. Computational Intelligence and Neuroscience, 10.
- Chan, P.K., Mahoney, M.V. and Arshad, M.H. (2003) A Machine Learning Approach to Anomaly Detection. Florida Institute of Technology, Tech. Rep. CS-2003-06.
- Amreen Sultana, A. and Jabbar, M.A. (2016) Intelligent Network Intrusion Detection System Using Data Mining Techniques. 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), 21 July 2016.
- Goldberg, D.E. (1989) Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Boston.
- Wafa, A. (2013) Applying Artificial Neural Network and Extended Classifier System for Network Intrusion Detection. International Arab Journal of Information Technology (IAJIT).
- Danane, Y. and Parvat, T. (2015) Intrusion Detection System Using Fuzzy Genetic Algorithm. International Conference on Pervasive Computing (ICPC). https://doi.org/10.1109/PERVASIVE.2015.7086963
- Kemmerer, R.A. and Vigna, G. (2002) Intrusion Detection: A Brief History and Overview. Computer, 35.
- Yin, G., Zhang, Y. and Zhao, Z. (2017) A Novel Computer Network Intrusion Detection Algorithm Based on OSVM and Context Validation. International Conference on Progress in Informatics and Computing (PIC).