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Wireless device identification with radiometric signatures
- in Proceedings of the 14th ACM international conference on mobile computing and networking, ser. MobiCom ’08
"... 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 ..."
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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. This research was performed under an appointment to the
PARADIS: Physical 802.11 Device Identification with Radiometric Signatures
"... This paper describes a technique that identifies an ieee 802.11 frame’s source network interface card through passive radio-frequency analysis. Our approach, called paradis, leverages minute imperfections of transmitter hardware that are acquired at manufacture and are present even in otherwise iden ..."
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This paper describes a technique that identifies an ieee 802.11 frame’s source network interface card through passive radio-frequency analysis. Our approach, called paradis, leverages minute imperfections of transmitter hardware that are acquired at manufacture and are present even in otherwise identical nics. These imperfections are transmitterspecific and manifest themselves as artifacts of the emitted signals. We measure artifacts of individual wireless frames in the modulation domain, identify a suite of differentiating features, and apply efficient 802.11-specific machine-learning based classification techniques to achieve significantly higher degrees of accuracy than prior best known schemes. We experimentally demonstrate effectiveness of paradis in differentiating between more than a hundred 100 identical ieee 802.11 nics, with an accuracy in excess of 99.99%. Our results also show that the accuracy of paradis is resilient against ambient noise and changing characteristics of the wireless channel. 1.
Fingerprinting Smart Devices Through Embedded Acoustic Components
"... The widespread use of smart devices gives rise to both security and privacy concerns. Fingerprinting smart de-vices can assist in authenticating physical devices, but it can also jeopardize privacy by allowing remote iden-tification without user awareness. We propose a novel fingerprinting approach ..."
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The widespread use of smart devices gives rise to both security and privacy concerns. Fingerprinting smart de-vices can assist in authenticating physical devices, but it can also jeopardize privacy by allowing remote iden-tification without user awareness. We propose a novel fingerprinting approach that uses the microphones and speakers of smart phones to uniquely identify an indi-vidual device. During fabrication, subtle imperfections arise in device microphones and speakers which induce anomalies in produced and received sounds. We ex-ploit this observation to fingerprint smart devices through playback and recording of audio samples. We use audio-metric tools to analyze and explore different acoustic fea-tures and analyze their ability to successfully fingerprint smart devices. Our experiments show that it is even pos-sible to fingerprint devices that have the same vendor and model; we were able to accurately distinguish over 93% of all recorded audio clips from 15 different units of the same model. Our study identifies the prominent acoustic features capable of fingerprinting devices with high suc-cess rate and examines the effect of background noise and other variables on fingerprinting accuracy. 1
Do You Hear What I Hear? Fingerprinting Smart Devices Through Embedded Acoustic Components
"... The widespread use of smart devices gives rise to privacy concerns. Fingerprinting smart devices can jeopardize privacy by allowing re-mote identification without user awareness. We study the feasibil-ity of using microphones and speakers embedded in smartphones to uniquely fingerprint individual de ..."
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The widespread use of smart devices gives rise to privacy concerns. Fingerprinting smart devices can jeopardize privacy by allowing re-mote identification without user awareness. We study the feasibil-ity of using microphones and speakers embedded in smartphones to uniquely fingerprint individual devices. During fabrication, sub-tle imperfections arise in device microphones and speakers, which induce anomalies in produced and received sounds. We exploit this observation to fingerprint smartphones through playback and recording of audio samples. We explore different acoustic features and analyze their ability to successfully fingerprint smartphones. Our experiments show that not only is it possible to fingerprint de-vices manufactured by different vendors but also devices that have the same maker and model; on average we were able to accurately attribute 98 % of all recorded audio clips from 50 different Android smartphones. Our study also identifies the prominent acoustic fea-tures capable of fingerprinting smart devices with a high success rate, and examines the effect of background noise and other vari-ables on fingerprinting accuracy.
ApPLICATION OF DUAL-TREE COMPLEX WAVELET TRANSFORMS TO BURST DETECTION AND RF FINGERPRINT CLASSIFICATION
, 2009
"... AFIT/DEE/ENG/09-12 The continued proliferation of affordable RF communication devices has greatly increased wireless user exposure and the need for improved security to protect against spoofing. This work addresses various Open Systems Interconnection (OSI) Physical (PHY) layer mechanisms to extract ..."
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AFIT/DEE/ENG/09-12 The continued proliferation of affordable RF communication devices has greatly increased wireless user exposure and the need for improved security to protect against spoofing. This work addresses various Open Systems Interconnection (OSI) Physical (PHY) layer mechanisms to extract and exploit RF waveform features (“fingerprints”) that are inherently unique to specific devices and that may be used for reliable de-vice classification to provide hardware specific identification (manufacturer, model, and/or serial number). Automatically detecting, identifying and locating RF commu-nication devices remains a challenging technical problem and consists of: 1) the selec-tion and generation of fundamental signal characteristics (amplitude, phase, and/or frequency), 2) the feasibility and repeatability of detecting and locating the start of a burst using selected waveform feature(s) amidst channel noise, 3) the identi-fication and robust extraction of distinguishable fingerprints–features that uniquely
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, 2014
"... This Open Access Dissertation is brought to you for free and open access by the Dissertations and Theses at ScholarWorks@UMass Amherst. It has ..."
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This Open Access Dissertation is brought to you for free and open access by the Dissertations and Theses at ScholarWorks@UMass Amherst. It has
1 TRIESTE: A Trusted Radio Infrastructure for Enforcing SpecTrum Etiquettes
"... Abstract — There has been considerable effort directed at developing “cognitive radio ” (CR) platforms, which will expose the lower-layers of the protocol stack to researchers, developers and the “public”. In spite of the great potential of such a radio platform, such “public ” development threatens ..."
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Abstract — There has been considerable effort directed at developing “cognitive radio ” (CR) platforms, which will expose the lower-layers of the protocol stack to researchers, developers and the “public”. In spite of the great potential of such a radio platform, such “public ” development threatens the success of these platforms: the proliferation of such wireless platforms, plus the open-source nature of their supporting software, is powerful but also dangerous. It is easily conceivable that inexpensive and widely available cognitive radios could become an ideal platform for abuse since the lowest layers of the wireless protocol stack are accessible to programmers. In order to regulate the future radio environment, this paper presents a framework, known as TRIESTE (Trusted Radio Infrastructure for Enforcing SpecTrum Etiquettes), which can ensure that radio devices are only able to access/use the spectrum in a manner that conforms to their privileges. In TRIESTE, two levels of etiquette enforcement mechanisms are employed. The first is an on-board mechanism that ensures trustworthy radio operation by restricting any potential violation operation from accessing the radio through a secure component located in each CR. External to individual cognitive radios, an infrastructure consisting of spectrum sensors monitors the radio environment, and reports measurements to spectrum police agents that punish CRs if violations are detected. I.
AccelPrint: Imperfections of Accelerometers Make
"... 4Co-primary authors Abstract—As mobile begins to overtake the fixed Internet access, ad networks have aggressively sought methods to track users on their mobile devices. While existing countermeasures and regulation focus on thwarting cookies and various device IDs, this paper submits a hypothesis t ..."
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4Co-primary authors Abstract—As mobile begins to overtake the fixed Internet access, ad networks have aggressively sought methods to track users on their mobile devices. While existing countermeasures and regulation focus on thwarting cookies and various device IDs, this paper submits a hypothesis that smartphone/tablet accelerometers possess unique fingerprints, which can be ex-ploited for tracking users. We believe that the fingerprints arise from hardware imperfections during the sensor manufacturing process, causing every sensor chip to respond differently to the same motion stimulus. The differences in responses are subtle enough that they do not affect most of the higher level func-tions computed on them. Nonetheless, upon close inspection, these fingerprints emerge with consistency, and can even be somewhat independent of the stimulus that generates them. Measurements and classification on 80 standalone accelerom-eter chips, 25 Android phones, and 2 tablets, show precision and recall upward of 96%, along with good robustness to real-world conditions. Utilizing accelerometer fingerprints, a crowd-sourcing application running in the cloud could segregate sensor data for each device, making it easy to track a user over space and time. Such attacks are almost trivial to launch, while simple solutions may not be adequate to counteract them.
Robust Stable Radiometric Fingerprinting for Wireless Devices
"... Abstract—We introduce a new method for radiometric fin-gerprinting that detects the unique variations in the antenna, oscillator properties, as well as the digital and analog interfaces of the radio by passively monitoring the radio packets. Several individual identifiers are used for extracting the ..."
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Abstract—We introduce a new method for radiometric fin-gerprinting that detects the unique variations in the antenna, oscillator properties, as well as the digital and analog interfaces of the radio by passively monitoring the radio packets. Several individual identifiers are used for extracting the unique physical characteristics of the radio, including the frequency offset, mod-ulated phase offset, in-phase/quadrature-phase offset from the origin, and magnitude. Our method provides stable and robust identification by developing individual identifiers (classifiers) that may each be weak (i.e., incurring a high prediction error) but their committee can provide a strong classification technique. We use two methods for combining the classifiers: (1) weighted vot-ing, and (2) maximum likelihood. Our hardware implementation and experimental evaluations over multiple radios demonstrate that our weighted voting approach can identify the radios with an average of 88 % detection probability and an average of 12.8 % probability of false alarm after testing only 5 frames. The probability of detection and probability of false alarms both rapidly improve by increasing the number of test frames. Index Terms — Wireless Security, RF Fingerprinting I.
