Hybrid Intrusion Detection Systems Based Mean-Variance Mapping Optimization Algorithm and Random Search
International journal of intelligent engineering and systems · 2023
Intrusion detection systems are critical in identifying and mitigating cyber threats.However, the intrusion data often contains insufficient features, which can adversely affect the classification accuracy of machine learning algorithms.To effectively select optimal features from intrusion attacks, a highly efficient model is required to extract highly correlated features.Nevertheless, traditional detection systems may experience low accuracy and high falsepositive rates.This paper proposes a hybrid model for improving intrusion detection systems using mean-variance mapping optimization (MVMO) and random search (MVMOR).The strategy of the proposed model is MVMO algorithm is used to search for the optimal feature subset.while, the random search is employed to optimize the hyperparameters of the machine learning algorithm (classifier).The proposed hybrid model seeks to optimize the feature selection and parameters of classifiers at some time.The performance of the hybrid model is evaluated on the NSL-KDD as a benchmark dataset.The proposed MVMOR achieves an accuracy of 88%, while the conventional MVMO achieved only an 80% accuracy rate.The empirical results show the proposed model has the potential to offer better protection against various cyber threats, thus making a valuable contribution to the field of Cybersecurity.