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

• URL: http://arxiv.org/abs/2104.01813v1
• Date: Mon, 5 Apr 2021 08:51:24 GMT
• Title: Semi-supervised Variational Temporal Convolutional Network for IoT Communication Multi-anomaly Detection
• Authors: Yan Xu, Yongliang Cheng
• Abstract summary: Internet of Things (IoT) devices are constructed to build a huge communications network. These devices are insecure in reality, it means that the communications network are exposed by the attacker. In this paper, we propose SS-VTCN, a semi-supervised network for IoT multiple anomaly detection.
• Score: 3.3659034873495632
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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