Electromagnetic signatures of WLAN cards and network security

Remley, Kate A.; Grosvenor, Chriss A.; Johnk, Robert T.; Novotný, David; Hale, Paul D.; McKinley, M.D.; Karygiannis, A.; Antonakakis, E.

doi:10.1109/isspit.2005.1577145

Cited by 62 publications

(29 citation statements)

References 5 publications

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“…Perhaps the most intuitive and straightforward way of radiometric analysis was studied by Remley [29], who visually identified differences between signals of different 802.11 transmitters. Gerdes et al [12] used physical layer signal processing to distinguished between wired Ethernet NICs based on fragments of their signals.…”

Section: Radiometric Identificationmentioning

confidence: 99%

Wireless device identification with radiometric signatures

Brik

Banerjee

Gruteser

et al. 2008

Proceedings of the 14th ACM International Conference on Mobile Computing and Networking

708

420

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We design, implement, and evaluate a technique to identify the source network interface card (NIC) of an IEEE 802.11 frame through passive radio-frequency analysis. This technique, called PARADIS, leverages minute imperfections of transmitter hardware that are acquired at manufacture and are present even in otherwise identical NICs. These imperfections are transmitter-specific and manifest themselves as artifacts of the emitted signals. In PARADIS, we measure differentiating artifacts of individual wireless frames in the modulation domain, apply suitable machine-learning classification tools to achieve significantly higher degrees of NIC identification accuracy than prior best known schemes.We experimentally demonstrate effectiveness of PARADIS in differentiating between more than 130 identical 802.11 NICs with accuracy in excess of 99%. Our results also show that the accuracy of PARADIS is resilient against ambient noise and fluctuations of the wireless channel.Although our implementation deals exclusively with IEEE 802.11, the approach itself is general and will work with any digital modulation scheme. *

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Section: Radiometric Identificationmentioning

confidence: 99%

Wireless device identification with radiometric signatures

Brik

Banerjee

Gruteser

et al. 2008

Proceedings of the 14th ACM International Conference on Mobile Computing and Networking

708

420

View full text Add to dashboard Cite

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“…Further, recent studies show that target's wireless card can be identified by its unique physical layer properties, e.g. electromagnetic signature [18] or timing [19]. However, we consider the adversary does not employ such means that can involve significant effort for tracking even a single target vehicle, such as deploying expensive cameras with density sufficient for desired tracking resolution, or employing specialized hardware to capture and process electromagnetic signatures.…”

Section: A Trust Assumptions and Adversary Models Consideredmentioning

confidence: 99%

AMOEBA: Robust Location Privacy Scheme for VANET

Sampigethaya

Huang³

et al. 2007

IEEE J. Select. Areas Commun.

409

321

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Abstract-Communication messages in vehicular ad hoc networks (VANET) can be used to locate and track vehicles. While tracking can be beneficial for vehicle navigation, it can also lead to threats on location privacy of vehicle user. In this paper, we address the problem of mitigating unauthorized tracking of vehicles based on their broadcast communications, to enhance the user location privacy in VANET. Compared to other mobile networks, VANET exhibits unique characteristics in terms of vehicular mobility constraints, application requirements such as a safety message broadcast period, and vehicular network connectivity. Based on the observed characteristics, we propose a scheme called AMOEBA, that provides location privacy by utilizing the group navigation of vehicles. By simulating vehicular mobility in freeways and streets, the performance of the proposed scheme is evaluated under VANET application constraints and two passive adversary models. We make use of vehicular groups for anonymous access to location based service applications in VANET, for user privacy protection. The robustness of the user privacy provided is considered under various attacks.

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“…A natural augmentation is to constraint the estimation result by utilizing our nonlinearity model in (3). The estimated symbols are over-fitted to the model to reduce the error perpendicular to the solution plane and than they are re-projected back to the corresponding transmit symbols.…”

Section: Convergence Rate Improvementmentioning

confidence: 99%

Wireless device identification in MIMO channels

Liu

Doherty

2009

2009 43rd Annual Conference on Information Sciences and Systems

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A data-aided iterative algorithm to estimate the nonlinearities of wireless emitters for Specific emitter Identification (SEI) is presented. To achieve robust estimation, Inter Symbol Interference (lSI) is removed by iteratively estimating the channel coefficients and nonlinear transmit symbols to achieve asymptotically unbiased estimation. The complexity of the iteration procedure is further reduced by increasing the step size of the iteration result. The algorithm is applicable to various communication systems where multiple amplitude level schemes are used such as QAM, OFDM, and PAM in MultipleInput Multiple-Output (MIMO) channels. Numerical results are shown which achieve nonlinearity estimation and radio emitter identification over empirical channel models using an Orthogonal Frequency Division Multiplexing (OFDM) MIMO system.

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Electromagnetic signatures of WLAN cards and network security

Cited by 62 publications

References 5 publications

Wireless device identification with radiometric signatures

Wireless device identification with radiometric signatures

AMOEBA: Robust Location Privacy Scheme for VANET

Wireless device identification in MIMO channels

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