  The acceptance of Internet of Things (IoT) applications and services has seen
an enormous rise of interest in IoT. Organizations have begun to create various
IoT based gadgets ranging from small personal devices such as a smart watch to
a whole network of smart grid, smart mining, smart manufacturing, and
autonomous driver-less vehicles. The overwhelming amount and ubiquitous
presence have attracted potential hackers for cyber-attacks and data theft.
Security is considered as one of the prominent challenges in IoT. The key scope
of this research work is to propose an innovative model using machine learning
algorithm to detect and mitigate botnet-based distributed denial of service
(DDoS) attack in IoT network. Our proposed model tackles the security issue
concerning the threats from bots. Different machine learning algorithms such as
K- Nearest Neighbour (KNN), Naive Bayes model and Multi-layer Perception
Artificial Neural Network (MLP ANN) were used to develop a model where data are
trained by BoT-IoT dataset. The best algorithm was selected by a reference
point based on accuracy percentage and area under the receiver operating
characteristics curve (ROC AUC) score. Feature engineering and Synthetic
minority oversampling technique (SMOTE) were combined with machine learning
algorithms (MLAs). Performance comparison of three algorithms used was done in
class imbalance dataset and on the class balanced dataset.
