Enhancing Security in Connected Medical IoT Networks Through Deep Learning-Based Anomaly Detection

In recent years, there has been an alarming increase in cyberattacks targeting connected medical devices. Distributed denial of service (DDoS) and botnet attacks are particularly common, and many vulnerabilities in IoT systems make these devices particularly vulnerable. Traditional intrusion detection techniques often fall short in addressing these threats. To overcome this challenge, we propose a deep learning-based intrusion detection system (IDS) for connected medical devices that utilizes four different architectures: multi-layer perceptron (MLP), long short-term memory (LSTM), convolutional neural network (CNN), and hybrid CNN-LSTM. We evaluated our system on the UNSW-NB15 and Edge-IIoTset datasets, and achieved a classification accuracy of 99.8% for binary classification and 96% for multiclass classification, with a false alarm rate of less than 2%. Our results show that deep learning can be an effective tool for detecting fraud attacks in connected medical devices. This research aims to enhance the security posture of medical IoT systems and mitigate potential risks.