Performance Evaluation of A Snort-Based Intrusion Detection System in A Hospital Network

Network security in hospital environments represents a critical challenge due to high traffic volumes and the sensitivity of medical data. This study aims to evaluate the performance of a Snort-based Intrusion Detection System (IDS) in detecting network attacks within the Mitra Keluarga Hospital infrastructure. The evaluation was conducted using an experimental approach by deploying Snort on a monitored server segment and performing simulated attacks, including port scanning, SSH brute force, ICMP flooding, and SQL injection. System performance was assessed based on detection respone time, detection rate, and alert consistency. The results demonstrate that the IDS successfully detected all tested attack scenarios, achieving respone times ranging from 0.4 to 1 second and a detection rate of 100% under the experimental conditions. However, potential false positives were identified in internal ICMP traffic, indicating the need for threshold parameter adjustment. These findings indicate that a Snort-based IDS is effective as an early attack detection mechanism for hospital networks and can be further enhanced through integration with centralized monitoring systems to support informed network security decision-making