Performance Comparison of Multi class SVM, Support Vector Machine, k-NN and Binary Classification for Intrusion Detection - Parasuraman, Kumar; Kumar, A. Anbarasa (International Journal of Computer Sciences and Engineering, 2018)
Intrusion detection is a fundamental part of security tools, for example, adaptive security appliances, intrusion detection systems, intrusion prevention systems and firewalls. Intrusion detection systems (IDS) plays a important role in detecting the attacks that occur in the PC or networks. Intrusion detection systems (IDS) are the network security mechanism that monitors network and system activities for malicious action.it become indispensable tool to keep information system safe and reliable. Different intrusion detection methods are used, but their performance is an problem. . Intrusion detection performance depends on accuracy, which needs to enhance to decrease false alarms and to increase the detection rate. Such procedures demonstrate limitations, are efficient for use in large datasets, for example, system, and network data. The intrusion detection system is used to analyzing huge traffic data, therefore efficient classification method is important to overcome the issue. Well-known machine learning techniques, namely, SVM, Multiclass SVM, k-NN, Binary Classification (BC) are applied. These techniques well known because of their capability in Classification. The NSL–knowledge discovery and data mining, dataset is used, which is considered a benchmark in the evaluation of intrusion detection mechanisms. The results indicate that Multiclass SVM outperforms other approaches.