article · IEEE Access
The Internet of Medical Things connects medical equipment to improve diagnostic accuracy and real-time operations, but this connectivity creates vulnerabilities to cyberattacks, especially Distributed Denial of Service attacks. Existing intrusion detection tools struggle with dynamic traffic and advanced threats. A hybrid intrusion detection system combines a Convolutional Neural Network for feature extraction with a Long Short Term Memory network for sequential data prediction. The system is deployed on a Raspberry Pi using a fog computing setup to bring decentralised processing closer to medical devices, cutting latency and boosting responsiveness. Evaluated on the IoTID20 and Edge-IIoTset datasets containing diverse traffic from genuine cyberattacks, such as SYN floods, Mirai, and SQL injection, the system achieved 99.92% accuracy, 99.91% precision, 99.99% recall, and an F1-score of 99.95%, demonstrating stronger performance than existing advanced detection methods.
Connected healthcare devices need strong cybersecurity to safeguard sensitive systems against disruptions caused by malicious network traffic. Providing real-time threat detection on lightweight local hardware helps protect medical operations from cyber threats like denial of service attacks without relying heavily on distant cloud servers, ensuring patient monitoring and examination technologies remain reliable and secure.
This technology could be applied by healthcare providers, hospital network administrators, and medical device manufacturers looking to secure Internet of Medical Things environments against cyberattacks. Given that the software was successfully tested using established intrusion datasets and implemented on low-cost Raspberry Pi hardware within a fog computing framework, it represents an applied and tested prototype that is approaching edge-deployment readiness for connected clinical networks.
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The expansion of the Internet of Medical Things (IoMT) has enhanced the accuracy, real-time functionality, connectivity, and intelligence of medical examination practices. However, increased interconnectivity of medical equipment has rendered IoMT networks susceptible to several cyberattacks, particularly the Distributed Denial of Service (DDoS) attacks. Current intrusion detection systems (IDS) are insufficiently focused to address and counteract these advanced threats. Furthermore, due to the dynamic nature of IoMT traffic, IDS has considerable difficulty preserving its current threat detection capabilities. This study presents a hybrid deep learning-based intrusion detection system for IoMT networks (HIDS-IoMT). The proposed model hybridizes the Convolutional Neural Network (CNN) for feature extraction and the Long Short Term Memory neural network (LSTM) for sequence data prediction. We implement the designed IDS on a Raspberry Pi device using a fog computing architecture, enabling decentralized processing closer to IoMT devices, thereby enhancing responsiveness and reducing latency. We evaluate the suggested approach for intrusion detection using the IoTID20 and the Edge-IIoTset datasets. These datasets comprise a substantial and varied assortment of traffic flows from actual DDoS attacks, including SYN floods, UDP floods, HTTP floods, and others. We evaluated our novel methodology against distinct DoS attacks such as DDoS, SQL injection, Vulnerability scanner, Scan Host Port, Mirai, etc. Our proposed model attains an accuracy of 99.92%, a precision of 99.91%, a recall rate of 99.99%, and an F1-score of 99.95%, outperforming the latest in advanced methodologies.
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DOI: 10.1109/access.2025.3543127
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