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| Title | How Can The Adversary Effectively Identify Cellular Iot Devices Using Lstm Networks? |
|---|---|
| ID_Doc | 29330 |
| Authors | Luo Z.J.; Pitera W.A.; Zhao S.; Lu Z.; Sagduyu Y.E. |
| Year | 2023 |
| Published | WiseML 2023 - Proceedings of the 2023 ACM Workshop on Wireless Security and Machine Learning |
| DOI | http://dx.doi.org/10.1145/3586209.3591394 |
| Abstract | The Internet of Things (IoT) has become a key enabler for connecting edge devices with each other and the internet. Massive IoT services provided by cellular networks offer various applications such as smart metering and smart cities. Security of the massive IoT devices working alongside traditional devices such as smartphones and laptops has become a major concern. Protecting these IoT devices from being identified by malicious attackers is often the first line of defense for cellular IoT devices. In this paper, we provide an effective attacking method for identifying cellular IoT devices from cellular networks. Inspired by the characteristics of Long Short-Term Memory (LSTM) networks, we have developed a method that can not only capture context information but also adapt to the dynamic changes of the environment over time. Experimental validation shows a high detection rate with less than 10 epochs of training on public datasets. © 2023 ACM. |
| Author Keywords | attacks; cellular iot devices; defenses; internet of things; lstm networks; recurrent neural networks; security |
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