Cyber threats are increasing not only in their volume but also in their sophistication and difficulty to detect. Attacks have become a national/global threat as they have targeted private and public, as well as government sectors over the years. This is a growing issue and organisations are taking steps to reduce, detect and prevent threats. To do this they need to use systems that are equipped with the capabilities to do either of those steps and develop them for the type of networks they use, for instance wired or wireless. One of these systems are Intrusion Detection Systems (IDS), which can be used as the first defence mechanism or a secondary defence mechanism of a threat or an attack. There are different types of attacks that can occur in a network, such as Denial of service (DoS)/Distributed Denial of Service (DDoS), port scanning, malware or ransomware and so forth that IDSs have a capability of detecting. Assisting in the mitigation of such attacks, there are also Intrusion Prevention Systems (IPS) whose role has a different purpose than that of IDSs. Unlike IDSs they not only detect threats but prevent them from disrupting the network, IPSs can be used in conjunction with IDSs to double the defences. This paper provides an overview of IDS and their classifications and IPS. It will detail typical benefits and limitations to using IDSs, IPSs and the hybrids (such as Intrusions Detection Prevention Systems (IDPSs and more)) which will be discussed further. It will also outline developments in the making using ML and how it is used to improve these systems and the dilemmas they produce and possible ways to counter act them.