Ensemble Deep Learning Intrusion Detection Model for Fog Computing Environments

Fog computing is decentralized architecture located between the cloud and devices that produce data. It acts as an intermediate layer between IoT devices and Cloud. Fog computing can perform substantial processing for the time sensitive IoT applications to reduce the latency. At the same time the Fog layer is exposed to various kinds of attacks. Deep learning-based intrusion detection system (IDS) can be suitable for fog computing paradigms for protecting the fog nodes from attacks. In this paper we have proposed a novel ensemble deep learning intrusion detection architecture for fog computing by combining two deep learning models such as traditional CNN and IDS-AlexNet model and showed this model gives high accuracy of attack detection. The respective model implementations were applied on the UNSW-NB15 datasets. By taking full advantage of different classifiers, the proposed deep learning-based multi-model ensemble method is shown to be accurate and effective for intrusion detection. Our proposed model shows that it outperformed various other traditional and recent models.