Exploring Machine Learning Algorithms for User Activity Inference from IoT Network Traffic
Exploring Machine Learning Algorithms for User Activity Inference from IoT Network Traffic
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DOI：
10.1109/mass58611.2023.00052
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发表时间：
2023-09
期刊：
影响因子：
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通讯作者：
Kuai Xu;Yinxin Wan;Xuanli Lin;Feng Wang;Guoliang Xue
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文献类型：
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作者：
Kuai Xu;Yinxin Wan;Xuanli Lin;Feng Wang;Guoliang Xue
智能家居中无处不在的异构物联网（IoT）设备的可用性及其与用户的交互为监控、理解、识别、学习和推断用户活动提供了独特的机会，用于安全监控、互联健康、节能以及其他颠覆性服务。我们对具有各种物联网设备的智能家居的物联网网络流量进行了分析，发现用户活动经常会触发部署在活动附近的多个物联网设备的重叠流量波。基于这一见解，我们采用小波分析将智能家居中的物联网网络流量分解为低、中、高频段，将用户活动触发的物联网流量波与物联网设备和云服务器之间的心跳信号等背景噪声区分开来。随后，我们从这些物联网流量波中提取了广泛的流量特征，并探索有监督的机器学习（ML）算法，用这些特征对各种用户活动进行分类。基于从真实智能家居环境中收集的标记用户活动和物联网网络流量数据，我们的实验表明，基于ml的算法能够使用物联网网络流量准确推断智能家居中的各种用户活动。
The availability of ubiquitous and heterogeneous Internet-of-Things (IoT) devices in smart homes and their interactions with users provide a unique opportunity to monitor, understand, recognize, learn, and infer user activities for safety monitoring, connected health, energy saving as well as other disruptive services. Our analysis on IoT network traffic from smart homes with a variety of IoT devices has discovered that user activities often trigger overlapping traffic waves from multiple IoT devices that are deployed near the activities. This insight leads us to adopt wavelet analysis to decompose IoT network traffic in smart homes into low, middle, and high frequency bands that distinguish IoT traffic waves triggered by user activities from background noises such as heartbeat signals between IoT devices and cloud servers. Subsequently, we extract a broad range of traffic features from these IoT traffic waves and explore supervised machine learning (ML) algorithms to classify various user activities with these features. Based on the labelled user activities and IoT network traffic data collected from real smart home environments, our experiments have demonstrated that the ML-based algorithms are able to use IoT network traffic to accurately infer various user activities in smart homes.