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An intrusion detection system (IDS) is considered critical for detecting threats, intrusions, and unauthorized access. IDS monitors massive network traffic that includes irrelevant and extravagant features that profoundly impact the system's efficiency and slow down the classification process for accurate decisions. Its effectiveness is tested over the various techniques that comprise an enormous volume of data and heavy network traffic. Many approaches, such as machine learning algorithms, data mining, swarm intelligence, and artificial neural networks (ANN), have been implemented for adequate and improved IDSs. This paper recommends a novel feature selection method using a genetic algorithm (GA) that determines the optimal feature subsets from the NSL-KDD dataset. Further, hybrid classification has been performed using logistic regression (LR) and decision tree (DT) to achieve a better detection rate (DR) and accuracy (ACC). This research applied and compared several meta-heuristic algorithms' performance to optimize the selected optimal features. The experimental results show that the grey wolf optimization (GWO) algorithm gives the best accuracy of 99.44% and DR of 99.36% with the reduction of features (=20) out of (=41). The results of the proposed work are compared with the existing feature selection methods to verify improved performance. [Display omitted] • Novel feature selection method using a genetic algorithm (GA) that determines the optimal feature subsets. • Hybrid classification to achieve a better detection rate (DR) and accuracy (ACC). • Compared several meta-heuristic algorithms' performance to optimize the selected optimal features. • The grey wolf optimization (GWO) algorithm gives the best accuracy of 99.44% and DR of 99.36% with the reduction of features (=20) out of (=41). • Optimization for improvement of IDS. [ABSTRACT FROM AUTHOR]

Sine Cosine Algorithm (SCA) that is one of the population-based metaheuristic optimization algorithms basically consists of the updating mechanism based on sine and cosine functions. In this algorithm, a few random and adaptive variables are also utilized for more effective motions of the candidate solutions. SCA has some drawbacks like other some metaheuristic algorithms. SCA tends to be stuck into the local regions in the search space and this affects negatively on the computational effort required to find the best solution point in the search space. This paper presents four different improved versions of SCA. The proposed improvements on original SCA are the innovations on the updating mechanism of SCA. To evaluate the performances of Improved Sine Cosine Algorithms (ImpSCAs), well-known numerical optimization problems including CEC 2014 test suite are used. Firstly, different analyses of the proposed ImpSCAs are dealt with such as the convergence analysis, search history analysis, trajectory analysis, average distance analysis, and computational complexity analysis. Secondly, the proposed four versions of ImpSCAs are compared with the original SCA for CEC 2014 benchmark problems with dimension sizes of 10D, 30D and 50D. Finally, original SCA and ImpSCAs are adapted to select optimal feature combination and they are tested for 10 feature selection datasets taken from the UCI machine learning repository. The benchmark results show that the performances of the ImpSCA 1 , ImpSCA 2 , and ImpSCA 4 are better than that of the original SCA. From the feature selection results, it is observed that three versions of ImpSCAs (except ImpSCA 3) outperform the original SCA in 80% of the datasets. Source codes of ImpSCAs are publicly available at https://github.com/uguryuzgec/ImpSCAs. • We focused to improve the exploration and exploitation ability of the original SCA. • The basic idea is based on the innovations on the update mechanism. • The ISCAs were evaluated using CEC2014 benchmark test suite. • The proposed ISCAs were tested for 10 popular UCI datasets. • Three versions of ISCAs (except ISCA 3) outperform the SCA in 80% of the datasets. [ABSTRACT FROM AUTHOR]