Intrusion Detection in Internet of Medical Things

The Internet of Things (IoT) is increasingly becoming an integral part of our daily lives. One of the most promising applications of IoT is in the healthcare sector, where these devices are referred to as the Internet of Medical Things (IoMT). However, as the use of IoT devices grows, so do concerns about their cybersecurity. In healthcare, the tradeoff between the benefits and security of IoMT devices must be carefully managed. Machine Learning (ML) offers various techniques to enhance the detection, prevention, and mitigation of cyberattacks. Recent benchmark datasets have been released, but comprehensive algorithm evaluations are still lacking. In this research, we aim to develop intrusion detection algorithms based on machine learning techniques. Our focus is on analyzing intrusion detection within medical environments. We compare various algorithms from different perspectives and demonstrate their effectiveness in detecting attacks on these systems.