Intrusion detection datasets are highly likely to contain numerous redundant, irrelevant, and noisy features that slow the performance of the machine learning techniques and classifiers that may be applied to them. The feature selection approach is used for reducing the number of features in intrusion detection datasets and eliminating those that are not important. One of the most powerful structured population approaches is the Cellular Automata approach, which is used to enhance the diversity and convergence of population-based optimization algorithms. In this work, the Cellular Automata approach, Mixed opposition-based learning, and the K-Nearest Neighbor classifier are incorporated with the Prairie dog optimization algorithm (PDO) in a new intrusion detection framework called Binary Opposition Cellular Prairie dog optimization algorithm (BOC-PDO). The proposed framework contains four key features. First, the Cellular Automata model is utilized to enhance the population of feasible solutions in the PDO. Second, four S-shaped and four V-shaped Binary Transfer Functions are used to convert the continuous solutions in BOC-PDO to binary ones. Third, the Mixed opposition-based learning approach is used at the end of the optimization loop of BOC-PDO to improve capacity for exploration. Fourth, the K-Nearest Neighbor classifier is used as the main learning model in BOC-PDO. Eleven famous intrusion detection datasets were employed in the evaluation of the effectiveness of BOC-PDO compared to eight popular binary optimization algorithms and four machine learning approaches. According to the overall simulation results, BOC-PDO scored the highest accuracy, best objective value, and fewest selected features for each of the eleven intrusion detection datasets. Besides, the reliability and consistency of the simulation results of BOC-PDO compared to the other tested algorithms were established using Friedman and Wilcoxon statistical tests.