In this paper, propose to use computational intelligence models to improve intrusion
detection system, the computational intelligence algorithms are used as preprocessing
steps for selecting most significant features from network data. Two computational intelligence algorithms, namely Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are implemented to generate subset of relevant features. The computational intelligence approaches have been applied to optimize the classification of algorithms. The most significant features obtained from computational intelligence is fed into
the classification algorithm. Novelty of this presents research of use computational intelligence algorithms namely Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) for handling dimensionality reduction. The dimensionality reduction is obstructed time processing of classification algorithms. Three classification algorithms
namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Naïve Bayes
(NB) are implemented for intrusion detection system. Benchmark datasets, namely, KDD
cup and NSL-KDD datasets are used to demonstrate and validate the performance of the
proposed model for intrusion detection. From the empirical results, it is observed that the
classification algorithm has improved the intrusion detection system with using computational intelligence algorithms. A comparative result analysis between the proposed model
and different existing models is presented. It is concluded that the proposed model has
outperformed of conventional models.