Pyramid Attention-Driven Cyber Attack Detection in IoMT Using Green Anaconda Metaheuristics

Medical devices, software, and networks have joined the Internet of Medical Things (IoMT) and have enhanced healthcare services but pose threats. IoMT devices are vulnerable to cyberattacks that can affect patient data as well as the operations of the healthcare organization, which requires an enhanced and effective intrusion detection system (IDS). This paper introduces a novel approach for detecting cyberattacks in IoMT environments using a deep learning model named pyramid attention network with the Green Anaconda algorithm (PANet-GAO). The approach has three steps as follows: 1) MinMax scaler normalization to process the data, 2) multiple discrete orthonormal S-transforms (MDOSTs) to extract the critical temporal-frequency features, and 3) pyramid attention network with the Green Anaconda algorithm (PANet-GAO) for accurate cyberattack detection. The MDOSTs can extract the dynamic information from the network traffic data in the time-frequency domain, and the feature space of the PANet model is optimized by GAO, which minimizes the feature redundancy to improve the classification accuracy. The proposed model was tested using the WUSTL-EHMS dataset, which mimics real-life IoMT traffic and different cyberattacks. The proposed PANet-GAO model's precision was 99.76%, its accuracy was 99.86%, its F1-score was 99.78%, and was better than traditional machine learning algorithms. Therefore, this work establishes that the proposed PANet-GAO approach can effectively protect IoMT environments from emerging cyber threats in real-time.