Computational Intelligence (CI) based real-time intrusion detection is currently gaining significant attention from the research groups. Computational intelligence system characteristics, such as adaptation, great computational speed, fault tolerance, and error resistance in the face of noisy information, suit the needs of creating a good model for intrusion detection. Since the 1990s, computational intelligence has been prominently found in many results to the issue of network intrusion detection. The achievements of the recent past have continued this importance and success. These improvements provide the benefit and ability of computational intelligence in network intrusion detection systems for operations like classification, generation of signature, selection of feature, deviation detection, and clustering. The paper aims to analyze intrusion detection through computational intelligence as well as a neural network. Existing research work related to intrusion detection systems is also analyzed in this paper. This work also encompasses the methods of computational intelligence, including fuzzy logic (FL), artificial neural network (ANN), and evolutionary computation (EC).