CNN-Based Autonomous Traffic Detection on Unknown Home Security Cameras
Shuhe Liu, Xiaolin Xu, Zhefeng Nan · 2022
As a popular IoT device, home security cameras (HSCs) generate large amounts of traffic, but the security mechanisms are weak, making them often exploited to launch DDoS attacks. Moreover, the lurking HSC device significantly threatens user privacy, so it is necessary to detect the HSC traffic. With the current service upgrade of HSC devices and the popularity of the open-source DIY community, the HSC types have become numerous. However, current traffic classification methods can only rely on complex feature extraction, expertise, and static datasets for identification, making it challenging to detect unknown devices in the open world. To solve this problem, we propose an autonomous update framework, called CATD-HSC, to constantly improve the classifier’s detection ability on unknown HSC devices. First, we use a CNN-based classifier model to train known HSC traffic. Then, we collect the classifier’s output as a new feature and use the threshold filtering approach to filter the unknown HSC traffic. Finally, we cluster the unknown HSC traffic by connecting the original feature to the generated feature. Moreover, we make the process autonomous to reduce the repeated trivial training. We evaluate the performance of CATD-HSC on both the UNSW open dataset and the real-world HSC traces. The updated model achieves an average accuracy rate of 99.3% and a recall rate of 99.4% for unknown HSC traffic in three interference environments. Our further evaluation shows that CATD-HSC can successfully implement the classifier’s autonomous update and dynamic classification on unknown HSC devices.