Deep Learning Based Device Classification Method for Safeguarding Internet of Things
Yantian Luo, Xu Chen, Ning Ge, Jianhua Lü · 2021 IEEE Global Communications Conference (GLOBECOM) · 2021
With the rapid development of 5G networks, a great amount of Internet of Things (IoT) devices are connected to the Internet. Most of these devices are cost limited and thus are easily compromised by attackers to launch distributed denial of service (DDoS) attacks. The traditional DDoS defense methods at server side can not adapt to this new challenge, thus access-side DDoS detection architecture is urgently needed. In this paper, we propose a deep learning (DL) based IoT device classification method to support fine-grained behavior modeling of malicious traffic and thus enable access-side DDoS detection. Different from traditional studies based on machine learning (ML) which need expertise feature engineering, we propose a time characteristics extraction method based on 1-D convolutional neural network to capture high level time series features automatically for better classification performance. To avoid the feature loss problem, we propose a feature enhancement method based on residual connection module. Experimental results verify the effectiveness of our method, which offers a meaningful gain in terms of both accuracy and macro F1 score over existing approaches.