These days, intelligent cybersecurity models based on machine learning and data mining techniques are prevalent. Several factors might affect the quality of these models, including the accuracy, the ability to train new models quickly, the quick decision-making process, the simplicity of the created models, and the model’s interpretability. Feature selection algorithms can help achieve all these characteristics by isolating the crucial features from the unimportant ones during the model creation phase. The current article proposes an intelligent intrusion detection model based on an improved cuckoo search algorithm which is a nature-inspired optimization algorithm. The improved cuckoo search algorithm proposed in this article may tolerate several bad steps toward determining the set of effective features that would preserve or maximize the capabilities of the produced classification models. The generalization ability of such an algorithm is examined by applying it to 10 benchmark datasets, and it showed superior outcomes compared with several nature-inspired attribute selection approaches. Later, the improved cuckoo search algorithm is used to develop intelligent intrusion detection systems using the well-known “NSL-KDD” dataset. The obtained outcomes are appealing regarding the general performance, the time required to develop the intelligent intrusion detection models, and the number of rules generated.