Energy-Aware and Explainable Automated Machine Learning for Anomaly Detection in Healthcare IoT

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Abstract
With the widespread adoption of healthcare Internet of Things (IoT) devices, the need for effective intrusion and anomaly detection has become pivotal in ensuring network security. However, the optimization of machine learning (ML) and deep learning (DL) models for these detection tasks frequently necessitates extensive computational resources, adversely affecting both temporal and energy efficiency. This article introduces an automated ML (AutoML) framework specifically tailored to enhance anomaly detection models, with a strategic focus on energy efficiency throughout the optimization process. The process commences with data preprocessing, followed by feature selection employing a combination of recursive feature elimination (RFE) and SHapley Additive exPlanations (SHAP) to identify important features for anomaly detection. Subsequently, a baseline multilayer perceptron (MLP) neural network model is trained, and hyperparameter optimization is executed in a constrained search space to mitigate energy consumption. The framework produces optimized models, which are assessed based on accuracy and energy consumption at various checkpoints, with the models demonstrating inferior performance systematically excluded based on predefined accuracy or energy consumption objectives. Experimental outcomes reveal that the pipeline effectively balances detection performance with energy consumption, with certain cases showing minimal accuracy losses (less than 1%) accompanied by substantial energy savings (over 60%), presenting a sustainable and resource-efficient approach to anomaly detection in IoT systems.
Funding Information
  • Engineering and Physical Sciences Research Council (EPSRC) through the National Edge AI Hub (EP/Y028813/1)
  • CHEDDAR: Communications Hub for Empowering Distributed ClouD Computing Applications and Research (EP/X040518/1)

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