Ensemble Learning based Anomaly Detection for IoT Cybersecurity via
  Bayesian Hyperparameters Sensitivity Analysis
Tin Lai Farnaz Farid Abubakar Bello Fariza Sabrina
The Internet of Things (IoT) integrates more than billions of intelligent
devices over the globe with the capability of communicating with other
connected devices with little to no human intervention. IoT enables data
aggregation and analysis on a large scale to improve life quality in many
domains. In particular, data collected by IoT contain a tremendous amount of
information for anomaly detection. The heterogeneous nature of IoT is both a
challenge and an opportunity for cybersecurity. Traditional approaches in
cybersecurity monitoring often require different kinds of data pre-processing
and handling for various data types, which might be problematic for datasets
that contain heterogeneous features. However, heterogeneous types of network
devices can often capture a more diverse set of signals than a single type of
device readings, which is particularly useful for anomaly detection. In this
paper, we present a comprehensive study on using ensemble machine learning
methods for enhancing IoT cybersecurity via anomaly detection. Rather than
using one single machine learning model, ensemble learning combines the
predictive power from multiple models, enhancing their predictive accuracy in
heterogeneous datasets rather than using one single machine learning model. We
propose a unified framework with ensemble learning that utilises Bayesian
hyperparameter optimisation to adapt to a network environment that contains
multiple IoT sensor readings. Experimentally, we illustrate their high
predictive power when compared to traditional methods.