Internet of Things (IoT) is transforming human lives by paving the way for
the management of physical devices on the edge. These interconnected IoT
objects share data for remote accessibility and can be vulnerable to open
attacks and illegal access. Intrusion detection methods are commonly used for
the detection of such kinds of attacks but with these methods, the
performance/accuracy is not optimal. This work introduces a novel intrusion
detection approach based on an ensemble-based voting classifier that combines
multiple traditional classifiers as a base learner and gives the vote to the
predictions of the traditional classifier in order to get the final prediction.
To test the effectiveness of the proposed approach, experiments are performed
on a set of seven different IoT devices and tested for binary attack
classification and multi-class attack classification. The results illustrate
prominent accuracies on Global Positioning System (GPS) sensors and weather
sensors to 96% and 97% and for other machine learning algorithms to 85% and
87%, respectively. Furthermore, comparison with other traditional machine
learning methods validates the superiority of the proposed algorithm.

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