Intrusion Detection Using Hybrid Enhanced CSA-PSO and Multivariate WLS Random-Forest Technique

The exponential growth in data communication and increase in network size have led to various intrusions and attacks. An Intrusion Detection System (IDS) can be provided as a crucial component of a network or database to ensure the security of data communication over a network. The network size is large, a large dataset may comprise more irrelevant, redundant, and high-dimensional features that impact feature classification, thus affecting the intrusion detection rate. This study presents a new hybrid enhanced normalised Crow Search Algorithm (CSA) and Particle Swarm Optimisation (PSO) technique to address feature selection issues and to classify global best features using a random-forest classifier. In the proposed algorithm, the benefits of the CSA between the search strategy and rapid convergence phenomenon of the PSO algorithm are utilised to select the global best solution in a large search space. A random-forest classifier is used to classify the features after they are updated with weight values for significant features, assessing the asymptotic variance of features and points that are closest to the optimal solution. The asymptotic features are subjected to the weighted least mean square (WLS) method to eliminate large deviations among the features. The random-forest classifier distinguishes between normal records and abnormal intrusion records. The performance assessment of the proposed hybrid IDS model is performed by utilising two datasets, which reveals that the proposed model outperforms other existing models. The simulation outcomes show higher accuracy rate, precision value, recall factor, and F1-Score, revealing the efficacy of the IDS model.