<p>Due to the widespread use of computer networks and the Internet, the number of intruders increases annually, and the integration, security, and access to digital sources are faced with constant threats. Thus, the importance of maintaining security in such environments and the need to design a defense system to discover different threats have made scholars conduct studies and present modern and efficient Intrusion Detection Systems (IDSs). Such systems work with data features and determine traffic conditions by analysing these features. The features present in each dataset would determine the type of traffic. The large number of features in the problem environment and the unpredictable behavior of the network have made intrusion detection the central problem in the security of computer networks. In addition, the presence of unnecessary features in large numbers has made the feature selection problem an essential one in the IDS. This paper proposes an IDS based on the Multi-Objective Farmland Fertility (MOFF) algorithm to perform feature selection on the intrusion detection dataset. In addition, the Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Tree (DT) have been used to estimate the ability of the selected features to make accurate predictions of attacks. Simulation results have shown that the proposed method reduces the time required for intrusion detection by reducing the number of features from 41 to 12, from 41 to 13, and from 48 to 12 on the KDD cup99, NSL-KDD, and UNSW-NB15 datasets, respectively. In addition, the classification’s accuracy values were obtained as 99.705%, 99.32%, and 99.20%, respectively, indicating an acceptable performance level. In addition, analyzing the calculation complexities shows that the proposed method is better than similar approaches.</p>