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

TL;DR

This paper introduces SS-VTCN, a semi-supervised deep learning model combining Variational Autoencoders and Temporal Convolutional Networks, to effectively detect multiple anomalies in IoT communication networks, improving over existing methods.

Contribution

The paper presents a novel semi-supervised model, SS-VTCN, specifically designed for multi-anomaly detection in IoT networks, leveraging both labeled and unlabeled data for improved accuracy.

Findings

Outperforms state-of-the-art semi-supervised methods in anomaly detection.

Effective in real-world smart home IoT datasets.

Demonstrates robustness in detecting diverse IoT communication anomalies.

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.