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The Internet of Medical Things (IoMT) has a network of interconnected medical devices to capture patients' health metrics and store them in a centralized server for analysis by medical experts. The security concerns in IoMT data are therefore very high. The attackers may inject malware into the IoMT data during its transmission. If the health parameters are affected by malware it may mislead the medical experts in making inferences about the patient’s health. IoMT devices are resource-constrained and require faster analysis of data for better medical assistance. Most of the classification techniques used earlier suffer from exhaustive time and resource consumption. Hence, developing an intelligent framework that could reduce the data size and classify the malware quickly is essential. This paper proposes a deep learning framework called Auto-encoder to encode the IoMT data. The encoded features are then given …