Entropy-based IoT Devices Identification
Entropy-based IoT Devices Identification
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DOI：
10.23919/apnoms50412.2020.9236963
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
Hung Nguyen-An;T. Silverston;Taku Yamazaki;T. Miyoshi
中科院分区：
文献类型：
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作者：
Hung Nguyen-An;T. Silverston;Taku Yamazaki;T. Miyoshi
物联网现在已经成为日常生活的一部分，并且已经出现了广泛的新型物联网应用程序，收集网络物理数据并提供有关环境的信息。由于预计物联网流量将占互联网流量的主要部分，因此必须描述物联网流量并识别每个设备，特别是在网络攻击的情况下。在本文中，我们提出了一种新的方法来识别物联网设备的流量熵的基础上。我们计算流量特征的熵值，并依靠机器学习算法对流量进行分类。我们的方法成功地识别设备在各种网络条件下的性能高达94%，在所有情况下。我们的方法对不可预测的网络行为也具有鲁棒性，异常会扩散到网络中。
The Internet of Things is now part of everyday life and there has been a wide range of novel IoT applications collecting cyber-physical data and providing information on the environment. As it is expected that the IoT traffic will count for a major part of the Internet traffic, it is essential to characterize the IoT traffic and to identify each device, and especially in the case of cyberattacks. In this paper, we present a new method to identify IoT devices based on traffic entropy. We compute the entropy values of traffic features and we rely on Machine Learning algorithms to classify the traffic. Our method succeeds in identifying devices under various network conditions with performances up to 94% in all cases. Our method is also robust to unpredictable network behavior with anomalies spreading into the network.