Device Fingerprinting in Wireless Networks: Challenges and Opportunities

By Qiang Xu, Rong Zheng, Walid Saad, Zhu Han. IEEE Communications Surveys and Tutorials, 2015

387 citations recorded by the source corpus.

Node forgery or impersonation, in which legitimate cryptographic credentials are captured by an adversary, constitutes one major security threat facing wireless networks. The fact that mobile devices are prone to be compromised and reverse engineered significantly increases the risk of such attacks in which adversaries can obtain secret keys on trusted nodes and impersonate the legitimate node. One promising approach toward thwarting these attacks is through the extraction of unique fingerprints that can provide a reliable and robust means for device identification. These fingerprints can be extracted from transmitted signal by analyzing information across the protocol stack. In this paper, the first unified and comprehensive tutorial in the area of wireless device fingerprinting for security applications is presented. In particular, we aim to provide a detailed treatment on developing novel wireless security solutions using device fingerprinting techniques. The objectives are three-fold: (i) to introduce a comprehensive taxonomy of wireless features that can be used in fingerprinting, (ii) to provide a systematic review on fingerprint algorithms including both white-list based and unsupervised learning approaches, and (iii) to identify key open research problems in the area of device fingerprinting and feature extraction, as applied to wireless security.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/0042f67d1bf1199db709e78b5edfa8b9f4201bb8

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Citations in the corpus

ORACLE: Optimized Radio clAssification through Convolutional neuraL nEtworks (2018, methodology): “Indeed, physical (PHY) layer, medium access control (MAC) layer, and upper layers have been utilized for radio fingerprinting [1].”
Deep Learning Convolutional Neural Networks for Radio Identification (2018, methodology): “A variety of features at the physical (PHY) layer, medium access control (MAC) layer, and upper layers have been utilized for radio fingerprinting [2].”
Indoor Positioning Systems Based on Visible Light Communication: State of the Art (2017, background): “nologies are becoming significant parts in embedded systems to provide low-cost, anytime, and anywhere connectivity [3].”
A Review of Radio Frequency Fingerprinting Techniques (2020, background): “dependent features cannot be used separately as a fingerprint because they are very sensitive to environmental changes [72].”