TCN enhanced novel malicious traffic detection for IoT devices

Abstract

With the development of IoT technology, more and more IoT devices are connected to the network. Due to the hardware constraints of IoT devices themselves, it is difficult for developers to embed security software into them. Therefore, it is better to protect IoT devices at the traffic level. The effect of malicious traffic detection based on neural networks is promising. Still, the slow computation brings some difficulties to deploying AI-based detection systems on edge servers. Time Convolutional Network (TCN) is a high-speed neural network suitable for massively parallel computation. In this paper, we propose Multi-class S-TCN, an improved network supporting multiple classifications based on TCN for the practical needs of IoT scenarios. Besides, we implement a complete IoT traffic security detection procedure based on deep packet inspection and protocol analysis. The proposed Multi-class S-TCN significantly improves the detection speed without degrading the detection effect. Experiments show that this work has better detection performance and faster detection speed compared to existing approaches, proving the effectiveness of the proposed detection flow and Multi-class S-TCN in IoT scenarios.

Keywords:

• Network security
• malicious detection
• TCN
• DPI
• IoT

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was partially supported by National Key R&D Program of China [grant number 2020YFC0832500], Ministry of Education - China Mobile Research Foundation [grant number MCM20170206], The Fundamental Research Funds for the Central Universities [grant number lzujbky-2019-kb51] and [grant number lzujbky-2018-k12], National Natural Science Foundation of China [grant number 61402210], Major National Project of High Resolution Earth Observation System [grant number 30-Y20A34-9010-15/17], State Grid Corporation of China Science and Technology Project [grant number SGGSKY00WYJS2000062], Program for New Century Excellent Talents in University [grant number NCET-12-0250], Strategic Priority Research Program of the Chinese Academy of Sciences with [grant number XDA03030100], Google Research Awards and Google Faculty Award.