IoT-based intrusion detection system for healthcare using RNNBiLSTM deep learning strategy with custom features

Abstract

The need for security in healthcare environments has become increasingly important due to the rise of cyber-attacks and data breaches. In order to address this issue, this paper proposes an Internet of Things (IoT)-based intrusion detection system for healthcare using a deep learning strategy with custom features. This paper proposes an IoT-based intrusion detection system for healthcare using a deep learning strategy with custom features. The proposed system utilizes the IoT technology to gather real-time data from various medical devices and sensors deployed in a healthcare environment. The system incorporates a recurrent neural network (RNN) and a bidirectional long short-term memory (BiLSTM) algorithm to detect and classify intrusion attempts. The custom features are extracted from the incoming data streams and used to train the deep learning models. The proposed system is evaluated on a dataset comprising different types of intrusion scenarios, and it achieves an accuracy of 99.16%, error rate of 0.008371%, sensitivity ratio of 99.89% and specificity ratio of 98.203% for IoTID20 with custom features using RNNBiLSTM. The results demonstrate the effectiveness of the proposed system in detecting and mitigating security threats in a healthcare environment. The system has the potential to improve patient privacy and security, ultimately leading to better healthcare outcomes.