IoT Traffic: Modeling and Measurement Experiments
IoT Traffic: Modeling and Measurement Experiments
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DOI：
10.3390/iot2010008
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发表时间：
2021-02
期刊：
影响因子：
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通讯作者：
Hung Nguyen-An;T. Silverston;Taku Yamazaki;T. Miyoshi
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文献类型：
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作者：
Hung Nguyen-An;T. Silverston;Taku Yamazaki;T. Miyoshi
我们现在在日常生活中使用物联网（IoT）。新型物联网设备收集网络物理数据并提供有关环境的信息。因此，物联网流量将占互联网流量的主要部分;然而，其对网络的影响仍然是未知的。由于资源受限或配置错误，物联网设备容易受到网络攻击。描述物联网流量并识别每个设备以监控物联网网络并区分合法和异常物联网流量至关重要。在这项研究中，我们部署了一个由多个物联网设备组成的智能家居测试平台来研究物联网流量。我们使用一种名为IoTTGen的新型物联网流量生成器工具进行了广泛的测量实验。该工具可以从多个设备生成流量，模拟不同网络条件下不同设备的大规模场景。我们通过计算流量参数的熵值和在行为形状图上直观观察流量来分析物联网流量属性。我们提出了一种新的方法来识别交通熵为基础的设备，计算交通特征的熵值。该方法依赖于机器学习来对流量进行分类。所提出的方法成功地识别设备的性能准确度高达94%，是鲁棒的不可预测的网络行为与网络中传播的流量异常。
We now use the Internet of things (IoT) in our everyday lives. The novel IoT devices collect cyber–physical data and provide information on the environment. Hence, IoT traffic will count for a major part of Internet traffic; however, its impact on the network is still widely unknown. IoT devices are prone to cyberattacks because of constrained resources or misconfigurations. It is essential to characterize IoT traffic and identify each device to monitor the IoT network and discriminate among legitimate and anomalous IoT traffic. In this study, we deployed a smart-home testbed comprising several IoT devices to study IoT traffic. We performed extensive measurement experiments using a novel IoT traffic generator tool called IoTTGen. This tool can generate traffic from multiple devices, emulating large-scale scenarios with different devices under different network conditions. We analyzed the IoT traffic properties by computing the entropy value of traffic parameters and visually observing the traffic on behavior shape graphs. We propose a new method for identifying traffic entropy-based devices, computing the entropy values of traffic features. The method relies on machine learning to classify the traffic. The proposed method succeeded in identifying devices with a performance accuracy up to 94% and is robust with unpredictable network behavior with traffic anomalies spreading in the network.