Abstract: The Internet of Medical Things (IoMT) advances healthcare capabilities but exposes critical cybersecurity vulnerabilities. This study evaluates five attack types—DoS, DDoS, MITM, Blackhole, and MQTT broker flooding—within an IEEE 802.11-based IoMT network simulated in NS-3, featuring Baxter Wireless Infusion Pump and Hexoskin Smart Health Shirt devices. However, some limited native support in NS-3 led to some abstractions designed to reflect real-world featured simulations. A Statistical Analysis of 540 network flows across throughput, one-way delay (OWD), packet delay variation (PDV), and packet loss revealed that the MITM attack was the most disruptive, with a statistically significant packet loss (mean = 11.50 %, p < 0.0001) and latency issues. Despite performance degradation, Bluetooth energy consumption remained constant (2.580 J), underscoring the inadequacy of energy metrics alone for anomaly detection. MITM compromises data integrity and timing, posing direct risks to patient safety. While blockchain enhances authentication, its computational demands challenge constrained IoMT devices. The study recommends mitigation strategies such as Deep Packet Inspection (DPI), Network Behavior Analysis (NBA), Anomaly Detection (NAD), and Federated Learning (FL), supported by encryption and packet cleansing. These approaches collectively offer a foundation for secure, resilient, and performance-aware IoMT deployments in healthcare environments. Keywords: Internet of Medical Things (IoMT), Networks, Security, Resilience, Network Simulator 3 (NS-3), Denial of Service (DoS), Distributed Denial-of-Service (DoS), Man-in-the-Middle (MITM), Message Queuing Telemetry Transport (MQTT), Wireless Infusion Pump (WIP), Smart Health Shirt (SHS), Blockchain and Federated Learning (FL).
Rajab et al. (Tue,) studied this question.