Network Intrusion Classification using Configuration Optimized Support Vector Machines

Cyber security has been undergoing various advancements in technology in recent years among which the use of big data analytics to process large network data is driving the change. The extraction and analysis of the network intrusion data using big data analytics have provided high detection accuracy with minimized complexity than the traditional data classification methods. Support Vector Machines (SVM) has been utilized extensively utilized for network intrusion classification and has provided high performance. However, the SVM based models have often suffered from problems of high training time and model complexity. This paper aims at developing a big data analytics model using optimized SVM to classify the intrusion datasets. In this model, the hybrid algorithm of Hyper-Heuristic Particle Swarm optimization (HHPSO) is derived for optimizing the configuration of the SVM. This can significantly reduce the model complexity and also reduce the training time. For achieving this objective, hyper-heuristic optimization is combined with Particle Swarm optimization (PSO) to optimize the margin parameter, kernel type and kernel parameter for enhancing accuracy and decreasing model complexity of the SVM model. Two datasets NSL-KDD and ISCX-IDS are used here to perform the experiments in MATLAB. The results indicate that the proposed model has achieved better performance than the compared models in terms of accuracy and time.