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.
Highlights
In recent decades, the development of wireless communication networks has lead to the use of portable devices anytime and anywhere
2 Thales Canada Inc. - TRT, Quebec City, Canada. 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]
Despite the advantages in security protocols and systems in the last decade, fast evolution of physical attacks by rogue guests to the ZigBee networks makes physical layer attacks prevention and countermeasures very complicated, because of the intrinsic importance of physical layer attacks in comparison with cryptanalytic attacks [5]
Summary
The development of wireless communication networks has lead to the use of portable devices anytime and anywhere. An approach to improve the security of data communication through a vulnerable network channel consists in defining RF Distinct Native Attributes (RF-DNA) features of hardware devices (PHY layers) [6], which are inherently unique for a given device [7]. In this paper, these RF-DNA features are analyzed and processed for the discrimination and rejection of spoofing devices.
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