An Intrusion Detection System for the Internet of Medical Things (IoMT): An Artificial Intelligence (AI) Approach

Emmanuel Araba, Paul Moggridge

Abstract


The Internet of Medical Things (IoMT) is a network of linked medical devices, including pacemakers, prosthetic limbs, glucometers, smartwatches, and more, integrated with software programs and healthcare infrastructure. IoMT has transformed healthcare by solving long-standing problems, but because of its design, it raises questions about the security and privacy of patient information. Finding reliable solutions to protect IoMT network traffic is vital because using older systems can disclose weaknesses and attract potential cyber threats. Intrusion detection systems (IDS) have recently become a key component of IoMT network security. Machine learning (ML) systems have demonstrated impressive efficacy in intrusion detection over the past few decades. Nevertheless, research examining the potential of ML algorithms for IDS in IoMT networks needs to be more comprehensive. In this regard, this research delves into the intricate realm of network traffic threat analysis, employing the CICDDoS2019 dataset encompassing 44,687 rows and 88 diverse columns. Through meticulous data preprocessing, missing values were addressed, and redundant columns were pruned, leading to a refined dataset of 62 columns. Post-data transformation and normalisation, machine learning implementations, specifically XGBoost and Random Forest algorithms, were employed for classification tasks. The initial XGBoost model showcased an accuracy of 97.62%, which, after hyperparameter optimisation, slightly rose to 97.71%. Concurrently, the Random Forest Classifier demonstrated an initial accuracy of 96.65%, which increased to 97.64% post-optimisation. Both models exhibited robust classification capabilities across varied network traffic types, with exceptional performance in classifying "BENIGN" traffic. The study underscores the pivotal role of hyperparameter tuning in enhancing machine learning model performance and presents insights into the nuanced classification of network traffic threat in the IoMT environment. 


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