Our increasing dependence on different types of networks leads us to make them more secure. Intrusion detection is very challenging in all of those networks. An Intrusion detection system (IDS) attempts to discover malicious activities in a network. More sophisticated and increasing number of attacks are targeted against computer networks. Several methods have been proposed to provide accurate intrusion detection. Use of Artificial Intelligence (AI) in intrusion detection systems is wellknown. AI-based IDSs may detect even unknown attacks. On the other hand, network throughput is increasing and IDSs should be able to handle the high volume of traffic in real-time. Different models have been proposed to improve the processing speed of these systems. Most studies consider IDSs in IP version 4 (IPv4). However, the migration to IP version 6 (IPv6) has already started and is inevitable. There are several security challenges in this migration process and hence, IDS becomes an essential tool for these networks. Evolution from conventional wired networks to other types of networks introduce another set of security threats. For example, cloud environment, grid computing and wireless networks open up several vulnerabilities which are easily exploited by attackers. Thus, the ability to protect such networks by IDS become increasingly challenging. This chapter discusses the applications of AI-based IDS in different environments.