Anomaly-based Intrusion Detection using Multiclass-SVM with Parameters Optimized by PSO
Intrusion Detection Systems (IDSs) play an important role in defending network systems from insider misuse as well as external attackers. Compared with misuse-based techniques, anomaly-based intrusion detection techniques perform well in detecting new attacks. Firstly, this paper proposes a feature selection algorithm based on SVM (termed FS-SVM) to reduce the dimensionality of sample data. Moreover, this paper presents an anomaly-based intrusion detection algorithm, i.e., Multiclass Support Vector Machine (MSVM) with parameters optimized by Particle Swarm Optimization (PSO) (termed MSVM-PSO), to detect anomalous connections.