This paper presents a cybersecurity solution designed to fortify Industrial Control Systems (ICS) against cyberattacks. The proposed solution integrates a Network-based Intrusion Detection System (NIDS) with a Decision Support System (DSS), leveraging machine learning to detect anomalies in network data and employing a filtering mechanism to reduce false alarms. The NIDS protects a simulated ICS testbed, detecting anomalies and forwarding them to the DSS for further analysis and selection of mitigation strategies. We outline the system architecture and showcase promising outcomes from a prototype implementation. Our proof of concept evaluation demonstrates high accuracy in detecting attack scenarios. Challenges such as detection delays between attacks and potential mitigations high-light areas for future improvement. This research contributes to bridging the gap between ML-based IDS and security solutions, paving the way for enhanced cybersecurity in ICS environments.