A Novel Hybrid Approach for Intrusion Detection Using Neuro-Fuzzy, SVM, and PSO

Abstract

This paper presents a novel method for optimising intrusion detection systems (IDS) by using two powerful techniques, namely ‘Principal component analysis (PCA)’ and ‘Particle swarm optimisation (PSO).’ Furthermore, the proposed approach is implemented on two categories of classifiers, Neuro-Fuzzy and support vector machines (SVM), which function on four widely used intrusion detection system datasets: CAIDA, DARPA, NSLKDD, and ISCX2012. Performance results are analysed individually based on a set of established evaluation criteria. Furthermore, the PSO algorithm is applied in search of the best combination of the outputs from the Neuro-Fuzzy and the SVM models, resulting in better attack detection accuracy with reduced false alarm rates. Another benefit of using PCA in the proposed method is that it considerably reduces the dimensions of the data by computing the principal components. This offers several advantages, such as reduced model complexity, training and execution time, memory usage, and model overfitting prevention. By focusing on the major components, PCA reduces noise in data to a certain extent, leading to increased classification accuracy and robustness. It also improves model interpretability by highlighting the key components. The application of PSO to find the most optimal parameters leads to the optimisation of the Neuro-Fuzzy and SVM models' parameters. The results achieved support that the proposed method for output combination in both Neuro-Fuzzy and SVM categories significantly enhances the accuracy of attack detection while reducing the false alarm rate.