Intrusion Detection System Using Deep Learning Techniques for Internet of Medical Things (IoMT)

The Internet of Things (IoT) has tremendously impacted people’s daily lives in recent years. Researchers and corporations also show interest in diverse IoT applications, such as the Internet of Medical Things (IoMT). In various aspects, security issues are the most important area of IoT, specifically for IoMT. The healthcare industry’s primary concern is safeguarding patient data from numerous intrusion attacks. It is essential to develop an effective and secure Intrusion Detection System (IDS) for IoMT. In this research article, we have proposed an efficient Intrusion Detection System (IDS) to identify intrusion attacks on the Internet of Medical Things (IoMT). We have developed a robust Intrusion Detection System (IDS) by employing a variety of Deep Learning (DL) techniques such as Simple Recurrent Neural Networks (S-RNN), Long Short-Term Memory Networks (LSTM) and Gated Recurrent Units (GRU) on a publicly available healthcare dataset. The experiment’s findings showed very high accuracy rates of intrusion detection using Simple Recurrent Neural Network (S-RNN), Long Short Term Memory Network (LSTM), Gated Recurrent Units (GRU) classification techniques compared to the state-of-art techniques. The overall accuracy of the experimented Intrusion Detection Systems was more than 99 % for all the implemented DL classification techniques.