Rogue device discrimination in ZigBee networks using wavelet transform and autoencoders
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Rogue device discrimination in ZigBee networks using wavelet transform and autoencoders Mohammad Amin Haji Bagheri Fard1
· Jean-Yves Chouinard1 · Bernard Lebel2
Received: 12 July 2019 / Accepted: 10 August 2020 © The Author(s) 2020
Abstract In modern wireless systems such as ZigBee, sensitive information which is produced by the network is transmitted through different wired or wireless nodes. Providing the requisites of communication between diverse communication system types, such as mobiles, laptops, and desktop computers, does increase the risk of being attacked by outside nodes. Malicious (or unintentional) threats, such as trying to obtain unauthorized accessibility to the network, increase the requirements of data security against the rogue devices trying to tamper with the identity of authorized devices. In such manner, focusing on Radio Frequency Distinct Native Attributes (RF-DNA) of features extracted from physical layer responses (referred to as preambles) of ZigBee devices, a dataset of distinguishable features of all devices can be produced which can be exploited for the detection and rejection of spoofing/rogue devices. Through this procedure, distinction of devices manufactured by the different/same producer(s) can be realized resulting in an improvement of classification system accuracy. The two most challenging problems in initiating RF-DNA are (1) the mechanism of features extraction in the generation of a dataset in the most effective way for model classification and (2) the design of an efficient model for device discrimination of spoofing/rogue devices. In this paper, we analyze the physical layer features of ZigBee devices and present methods based on deep learning algorithms to achieve high classification accuracy, based on wavelet decomposition and on the autoencoder representation of the original dataset. Keywords Physical layer · Wireless networks · ZigBee devices · Data preamble · RF-DNA · Autoencoder learning · Wavelet-transform
1 Introduction In recent decades, the development of wireless communication networks has lead to the use of portable devices anytime and anywhere. This desired wireless device portability for legitimate users, has also lead to vulnerability threats, like eavesdropping of unauthorized listeners, resulting in increasing the risks of information leakage for instance. M. A. Haji Bagheri Fard
[email protected] Jean-Yves Chouinard [email protected] Bernard Lebel [email protected] 1
Department of Electrical and Computer Engineering, Universit´e Laval, Quebec City, Canada
2
Thales Canada Inc. - TRT, Quebec City, Canada
Consequently, different security protocols such as WiFi Protected Access (WPA) and WPA2 provided a higher degree of security for short or high range radio communication systems over the last years [1]. In 2019, the Wi-Fi Alliance presented a new standard, WPA3, enhancing the security level in communication systems [2]. One of the communication protocols is ZigBee, introduced in 1999 [3],
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