  The increased reliance on the Internet and the corresponding surge in
connectivity demand has led to a significant growth in Internet-of-Things (IoT)
devices. The continued deployment of IoT devices has in turn led to an increase
in network attacks due to the larger number of potential attack surfaces as
illustrated by the recent reports that IoT malware attacks increased by 215.7%
from 10.3 million in 2017 to 32.7 million in 2018. This illustrates the
increased vulnerability and susceptibility of IoT devices and networks.
Therefore, there is a need for proper effective and efficient attack detection
and mitigation techniques in such environments. Machine learning (ML) has
emerged as one potential solution due to the abundance of data generated and
available for IoT devices and networks. Hence, they have significant potential
to be adopted for intrusion detection for IoT environments. To that end, this
paper proposes an optimized ML-based framework consisting of a combination of
Bayesian optimization Gaussian Process (BO-GP) algorithm and decision tree (DT)
classification model to detect attacks on IoT devices in an effective and
efficient manner. The performance of the proposed framework is evaluated using
the Bot-IoT-2018 dataset. Experimental results show that the proposed optimized
framework has a high detection accuracy, precision, recall, and F-score,
highlighting its effectiveness and robustness for the detection of botnet
attacks in IoT environments.
