Machine learning (ML) provides effective solutions to develop efficient intrusion detection
system (IDS) for various environments. In the present paper, a diversified study of various
ensemble machine learning (ML) algorithms has been carried out to propose design of an
effective and time-efficient IDS for Internet of Things (IoT) enabled environment. In this
paper, data captured from network traffic and real-time sensors of the IoT-enabled smart
environment has been analyzed to classify and predict various types of network attacks. The …

Abstract Machine learning (ML) provides effective solutions to develop efficient intrusion
detection systems (IDS) for various environments. A diversified study of various ensemble
machine learning (ML) algorithms has been carried out to propose the design of effective
and time-efficient IDS for the Internet of Things (IoT) enabled environment. Data captured
from network traffic and real-time sensors of the IoT-enabled smart environment has been
analyzed to classify and predict various types of network attacks. The performance of …