Connection Science // 2022
TCN Enhanced Novel Malicious Traffic Detection for IoT Devices
Xin Liu // Ziang Liu // Yingli Zhang // Wei Zhang // Ding Lv // Qingguo Zhou
This journal article studies novel malicious traffic detection for IoT devices with a temporal convolutional network based design, focusing on application-layer protocol identification, feature extraction, and efficient classification in realistic IoT traffic settings.
Overview
This work addresses malicious traffic detection in IoT environments with a temporal convolutional network based pipeline. It emphasizes practical protocol-aware traffic processing and detection accuracy under realistic attack traffic.
Research context
The paper represents the earlier network-security side of Ziang Liu’s work, preceding later research on software supply chain security and open-source license compliance.