A Hybrid Machine Learning Method in detecting anomalies in IoT at the fog layer

Abstract

With the rapid growth and utilization of IoT devices around the world, attacks on these devices are also increasing thereby posing a security and privacy issue for industry providers and end-users alike. A common way to detect anomaly behaviour is to analyze the network traffic and categorize the outcome into benign and malignant traffic. With an increase in network traffic and sophistication of attacking techniques daily, there is a need for a state-of-the-art pattern recognition technique that can handle this ever increasing and ever-changing traffic and can also improve over time as attacks become more sophisticated. This research paper proposes a hybrid model for anomaly detection at the IoT fog layer using an ANN as a base model and several binary classifiers (which served as meta-classifiers) connected in series. The proposed model was tested and evaluated on a dataset of ‘x’ observations, demonstrating that such a model is both highly effective and efficient in detecting IoT network traffic anomalies.