Industrial Control Systems (ICSs) are moving from dedicated communications to Ethernet-based interconnected networks, placing them at risk of cyber attack. ICS networks are typically monitored by an Intrusion Detection System (IDS), however traditional IDSs do not detect attacks which disrupt the control flow of an ICS. ICSs are unique in the repetition and restricted number of tasks that are undertaken. Thus there is the opportunity to use Process Mining, a series of techniques focused on discovering, monitoring and improving business processes, to detect ICS control flow anomalies. In this paper we investigate the suitability of various process mining discovery algorithms for the task of detecting cyber attacks on ICSs by examining logs from control devices. Firstly, we identify the requirements of this unique environment, and then evaluate the appropriateness of several commonly used process discovery algorithms to satisfy these requirements. Secondly, the comparison was performed and validated using ICS logs derived from a case study, containing successful attacks on industrial control systems. Our research shows that the Inductive Miner process discovery method, without the use of noise filtering, is the most suitable for discovering a process model that is effective in detecting cyber-attacks on industrial control systems, both in time spent and accuracy.