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The consumer Internet of Things (IoT) have developed in recent years. MassIoT devices are constructed to build a huge communications network. But thesedevices are insecure in reality, it means that the communications network areexposed by the attacker. Moreover, the IoT communication network also faceswith variety of sudden errors. Therefore, it easily leads to that is vulnerablewith the threat of attacker and system failure. The severe situation of IoTcommunication network motivates the development of new techniques toautomatically detect multi-anomaly. In this paper, we propose SS-VTCN, asemi-supervised network for IoT multiple anomaly detection that works welleffectively for IoT communication network. SS-VTCN is designed to capture thenormal patterns of the IoT traffic data based on the distribution whether it islabeled or not by learning their representations with key techniques such asVariational Autoencoders and Temporal Convolutional Network. This network canuse the encode data to predict preliminary result, and reconstruct input datato determine anomalies by the representations. Extensive evaluation experimentsbased on a benchmark dataset and a real consumer smart home dataset demonstratethat SS-VTCN is more suitable than supervised and unsupervised method withbetter performance when compared other state-of-art semi-supervised method.

Semi-supervised Variational Temporal Convolutional Network for IoT Communication Multi-anomaly Detection