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.
