  The consumer Internet of Things (IoT) have developed in recent years. Mass
IoT devices are constructed to build a huge communications network. But these
devices are insecure in reality, it means that the communications network are
exposed by the attacker. Moreover, the IoT communication network also faces
with variety of sudden errors. Therefore, it easily leads to that is vulnerable
with the threat of attacker and system failure. The severe situation of IoT
communication network motivates the development of new techniques to
automatically detect multi-anomaly. In this paper, we propose SS-VTCN, a
semi-supervised network for IoT multiple anomaly detection that works well
effectively for IoT communication network. SS-VTCN is designed to capture the
normal patterns of the IoT traffic data based on the distribution whether it is
labeled or not by learning their representations with key techniques such as
Variational Autoencoders and Temporal Convolutional Network. This network can
use the encode data to predict preliminary result, and reconstruct input data
to determine anomalies by the representations. Extensive evaluation experiments
based on a benchmark dataset and a real consumer smart home dataset demonstrate
that SS-VTCN is more suitable than supervised and unsupervised method with
better performance when compared other state-of-art semi-supervised method.
