The network traffic in the intrusion detection system (IDS) has unpredictable behaviour due to the high computational power. The complexity of the system increases; thus, it is required to investigate the enormous number of features. However, the features that are inappropriate and (or) have some noisy data severely affect the performance of the IDSs. In this study, we have performed feature selection (FS) through a random forest algorithm for reducing irrelevant attributes. It makes the underlying task of intrusion detection effective and efficient. Later, a comparative study is carried through applying different classifiers, e.g., k Nearest Neighbour (k-NN), Support Vector Machine (SVM), Logistic Regression (LR), decision tree (DT) and Naive Bayes (NB) for measuring the different IDS metrics. The particle swarm optimization (PSO) algorithm was applied on the selective features of the NSL-KDD dataset, which cut down the false alarm rate and enhanced the detection rate and the accuracy of the IDS as compared with the mentioned state-of-the-art classifiers. This study includes the accuracy, precision, false-positive rate and the detection rate as performance metrics for the IDSs. The experimental results show low computational complexity, 99.32% efficiency and 99.26% detection rate on the selected features (=10) out of a complete set (= 41).

由于高计算能力，入侵检测系统（IDS）中的网络流量具有不可预测的行为。系统的复杂性增加；因此，需要研究大量的功能。但是，不合适的功能和（或）包含一些嘈杂的数据会严重影响IDS的性能。在这项研究中，我们通过随机森林算法执行了特征选择（FS），以减少不相关的属性。它使入侵检测的基本任务有效而高效。后来，通过应用不同的分类器进行了比较研究，例如k最近邻（k-NN），支持向量机（SVM），逻辑回归（LR），决策树（DT）和朴素贝叶斯（NB）用于测量不同的IDS指标。将粒子群优化（PSO）算法应用于NSL-KDD数据集的选择性特征，与提到的最新状态相比，该算法可降低误报率并提高IDS的检测率和准确性。艺术分类器。这项研究包括准确性，准确性，假阳性率和检测率作为IDS的性能指标。实验结果表明，对整套特征（= 41）中所选特征（= 10）的计算复杂度较低，效率为99.32％，检测率为99.26％。