An LSTM based Malicious Traffic Attack Detection in Industrial Internet

Current Industrial Internet faces serious threats where attackers propagate malicious flows, resulting in communication failures in the Industrial Internet. In this work, we propose a practical and novel method to detect malicious traffic attack in real time with high accuracy. Our primary idea is to capture network flow, extract adequate network flow features, construct a long short-term memory (LSTM) based deep learning model, and identify the property of the corresponding network flow. Whether the network suffers attack or not is then determined according to the detection results. The corresponding prototype is also implemented in the Industrial Internet which is equipped with Software Defined Networking (SDN). Experimental results validate that the proposed method is effective in defending against malicious traffic attack in real-world network.