Intrusion Detection for Smart Home Security Based on Data Augmentation with Edge Computing

Smart home is an indispensable part of Internet of Things(IoT) owing to the prompt development and application of smart devices. However, the data collected from smart homes usually need to be processed by a cloud server, which means there is a risk of leaking the privacy of users during the transmission. In this situation, edge computing is considered to be an ideal platform for smart home, which enable data to be processed at edge nodes. Unfortunately, because of unsecured Wi-Fi connection and smart devices, edge nodes also have the possibility to encounter malicious attacks. Hence, in this paper, we designed an intrusion detection system (IDS) to be deployed on edge nodes. We convert network traffic to images which are applied to train a convolutional neural network (CNN) to classify the categories of network traffic. Furthermore, Auxiliary Classifier Generative Adversarial Network (AC-GAN) is adopted to generate synthesized samples to expand the intrusion detection dataset. We experiment on the UNSW-NB15 dataset which contains substantial network traffic about the normal and anomalies. The proposed scheme is effective to minor categories of which precision could be improved 12%. Besides, the precision can reach 96% in binary classification about normal and anomaly.