User Behavior Classification in Encrypted Cloud Camera Traffic

Home surveillance cameras have been widely used in recent years, however, what comes next is the increasing events of users' privacy disclosure. Most cameras use video compression technologies such as H.26X and MPEG to reduce the stream size during transmission on the purpose to transfer higher definition (HD) video with limited bandwidth. Although traffic is encrypted, these video differential encoding techniques can cause traffic patterns to change as users' behavior changes. Therefore, it is very meaningful to mine user privacy behavior hidden behind the surveillance traffic based on traffic statistical features. In this paper, we collected a large amount of surveillance video traffic, including 9 kinds of daily life behaviors, such as watching TV, switching door, sweeping the floor, etc. According to frequency distribution sequences of packet length, probability transition matrix of packet length and other statistical features, we used a variety of models to classify users' daily life behaviors. The results show that the random forest classifier and the AlexNet classifier can achieve a macro-averaging F1-score of 97.23% and 97.89% respectively. The performance of our approach reveals that the users' daily routine can be accurately constructed only through video traffic, which is a potentially huge security issue for the users. Moreover, this work has important reference value for camera manufacturers to improve user privacy protection.