Deep Learning Technique to Identify the Malicious Traffic in Fog based IoT Networks

Abstract

The network of devices known as the Internet of Things (IoT) consists of hardware with sensors and software. These devices communicate and exchange data through the internet. IoT device-based data exchanges are often processed at cloud servers. Since the number of edge devices and quantity of data exchanged is increasing, massive latency-related concerns are observed. The answer to these issues is fog computing technology. Fog computing layer is introduced between the edge devices and cloud servers. Edge devices can conveniently access data from the fog servers. Security of fog layer devices is a major concern. As it provides easy access to different resources, it is more vulnerable to different attacks. In this paper, a deep learning-based intrusion detection approach called Multi-LSTM Aggregate Classifier is proposed to identify malicious traffic for the fog-based IoT network. The MLAC approach contains a set of long short-term memory (LSTM) modules. The final outcomes of these modules are aggregated using a Random Forest to produce the final outcome. Network intrusion dataset UNSW-NB15 is used to evaluate performance of the MLAC technique. For binary classification accuracy of 89.40% has been achieved using the proposed deep learning-based MLAC model.