A Blended Deep Learning Intrusion Detection Framework for Consumable Edge-Centric IoMT Industry

Abstract

The demand for medical sensors in the Smart Healthcare System (SHS) creates an intelligent Internet of Medical Things (IoMT) system. This system plays an important role in detecting the vital parameters of the human body. However, security and privacy issues in terms of network vulnerability have arisen due to the transmission of data and lack of control over the data. The Intrusion Detection System (IDS) is one of the security solutions to identify various threats and vulnerabilities in the consumable edge-centric IoMT industry. Several IDS techniques have been developed in previous years. However, a real-time and highly accurate attack detection system in the edge-centric IoMT industry is needed. This paper proposes a blended deep learning framework that leverages the strengths and capabilities of different deep learning architectures. The proposed model combined Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to recognize the latest intruders accurately and defend the healthcare data. The major outcome of the proposed framework is to detect different attacks during data transmission at the edge of the network with high accuracy and efficiency. The proposed model was analyzed on the CSE-CIC-IDS 2018 systematic dataset containing two distinct classes of profiles. The experimental results demonstrate that the proposed framework’s accuracy is higher than the existing approach.