Abstract: Intrusion detection is a mechanism used to protect a
system and analyse and predict the behaviours of system users. An
ideal intrusion detection system is hard to achieve due to
nonlinearity, and irrelevant or redundant features. This study
introduces a new anomaly-based intrusion detection model. The
suggested model is based on particle swarm optimisation and
nonlinear, multi-class and multi-kernel support vector machines.
Particle swarm optimisation is used for feature selection by applying
a new formula to update the position and the velocity of a particle;
the support vector machine is used as a classifier. The proposed
model is tested and compared with the other methods using the KDD
CUP 1999 dataset. The results indicate that this new method achieves
better accuracy rates than previous methods.