## 1 Introduction
A considerable number of research efforts have been devoted to addressing the feature selection issue, which entails the selection of a vast spectrum of features [11]. In this regard, various approaches to feature selection can be broadly categorized as supervised and unsupervised methods. The former group of methods is divided into wrapper, filter, and embedded methods. The wrapper-based algorithm [12] uses a classifier to assess the relevance of the selected data subsets to ultimately specify the optimal set of features that contribute to higher accuracy. Filter-based feature selection approaches [13; 14], on the other hand, are based on careful selection of appropriate feature combinations to be used in training the IDS. Compared to the wrapper approach, filter-based methods are more efficient in computing. Embedded methods [11] can be defined as combinations of the filter and wrapper methods. In these methods, the classifier specifies the weights and importance of the features during training. The significance of this study lies in the identification of an IDS through the utilization of contemporary machine learning techniques for the purpose of enhancing cybersecurity. The proposed work addresses two primary concerns: feature filtration and the intelligent detection system. The process of feature filtration is an important matter as it aims to identify the most suitable set of network features in order to enhance the accuracy of detection and mitigate the occurrence of false detections. The intelligent detection system integrates intelligent approaches, namely the extended classifier system (XCS) and the genetic algorithm (GA). The GA assumes the responsibility of generating new classifiers within the system, which serves as the central component of the XCS during the process of rule matching. Based on the preceding discourse, it can be asserted that feature filtration is adequate for managing extensive volumes of data, such as those found in the KDD99 dataset. This paper presents fuzzy clustering using the local approximation of membership (FLAME) algorithm as an effective feature filtration approach for detecting cyberattacks in networks with high-volume traffic, such as KDD99 data. Thus, the proposed detection system is based on feature filtration using FLAME and a detection model using the XCS, with the entire enhancement of GA by applying the cuckoo search algorithm (CS). The main contributions of the proposed work are as follows:  
1. Recap the state-of-the-art works concerning feature selection algorithms utilized in ID. 2. Developing a new feature filtration method based on FLAME to specify the optimal subset of features that affect attacks detection and eliminate the features that raise a false alarm. 3. Propose a novel modification of the GA that integrates the cuckoo search algorithm as a selection method, aiming to improve the efficiency of the IDS classifier and attack detection. 4. Evaluate the proposed feature selection for IDS using KDD99. 5. Evaluate the proposed IDS and compare its results with the state-of-the-art works in terms of the detection rate (DR), accuracy (AC), and the false alarm rate (FAR) using KDD99. This paper is organized into six sections. Section 2 reviews the previous literature on feature selection in IDSs. Section 3 outlines the research background, and Section 4 describes the proposed IDS. Section 5 presents the experimental results and evaluations. Section 6 concludes this paper by highlighting future research directions.