Intrusion Detection in IoT Based Health Monitoring Systems

The internet of things (IoT) is making its impact in every possible field like agriculture, healthcare, automobile, traffic monitoring, and many others. Especially in the field of healthcare, IoT has numerous benefits. It has introduced the concept of remote monitoring of patients with the help of IoT devices. These devices are turning out to be a game-changer and are helping healthcare professionals monitor patients and suggest recommendations with the help of data obtained from connected devices or sensors. Telemedicine, which helped provide remote medical services to patients, has gained importance, especially during this COVID-19 pandemic. It has helped the patients have online consultations with the doctor during the lockdown period, decreasing the need for unwanted hospital visits during pandemic times. Since these IoT-related networks are used daily, from health monitoring wearables to smart home systems, they must be protected against security threats. Thus, intrusion detection System is significant in identifying intrusions over an IoT network. intrusion detection Systems can be deployed by utilizing Machine Learning, and deep learning approaches. This paper aims to implement various algorithms on the BoT-IoT dataset. Moreover, their performance measures are compared and analyzed.

Keywords: BoT-IoT dataset, Intrusion Detection, Machine Learning algorithms.