Enhancing the Performance of an Intrusion Detection System Through Multi-Linear Dimensionality Reduction and Multi-Class SVM
B. Kavin Kumar, M.S.V.S. Bhadri Raju, Bulusu Vardhan · International journal of intelligent engineering and systems · 2018
With the huge development of the usage of computer over network and advancement in applications running on various platforms captures the attention towards network security.The Intrusion Detection System (IDS) plays a vital role in detecting anomalies and attacks in the network.Earlier approaches of IDS relied on Machine Learning (M L) techniques.Due to some limitations, a better approach is needed.A combination of Machine Learning techniques and data preprocessing is an effective approach for IDS.In this work, a new dimensionality reduction technique combined with the Multi-class SVM (Support Vector Machine) is proposed for intrusion detection.In the proposed model, Multi-Linear Dimensionality Reduction (ML-DR) is proposed as a feature extraction technique to reduce the dimension in order to shorten the training time.A Multi-class SVM (M-SVM) is used to detect whether the action is an attack or not.Here the Multi-class SVM is adopted to perform multi-attack classification in a layered fashion.Radial Basis Function Kernel is used as a SVM kernel.NSL-KDD data set is used for the performance evaluation of the proposed approach.The performance metrics such as classification accuracy, false alarm rate and the correlation coefficient are evaluated to measure its efficiency.In comparison with other detection approaches, the experimental results show that the proposed model outperforms the higher classification accuracy.