DeMiCPU: Device Fingerprinting with Magnetic Signals Radiated by CPU

Cheng, Yushi; Ji, Xiaoyu; Zhang, Juchuan; Xu, Wenyuan; Chen, Yichao

doi:10.1145/3319535.3339810

Cited by 55 publications

(19 citation statements)

References 16 publications

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“…Fingerprinting by Mobile-specific Sensors. Although studies have shown that some fingerprinting techniques do not work well on mobile devices [53,57], recent work has proposed new fingerprinting techniques via mobile-specific sensor APIs, such as motion [20,34,35,108], proximity [33], ambient-light sensors [14], and magnetometers [27,65]. OmniCrawl instruments these APIs to measure their use in the wild, even though, except for motion, the APIs are by default disabled in modern browsers (our browsers).…”

Section: Tracking On Mobile Phonesmentioning

confidence: 99%

OmniCrawl: Comprehensive Measurement of Web Tracking With Real Desktop and Mobile Browsers

Cassel

Lin

Buraggina

et al. 2021

Proceedings on Privacy Enhancing Technologies

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Over half of all visits to websites now take place in a mobile browser, yet the majority of web privacy studies take the vantage point of desktop browsers, use emulated mobile browsers, or focus on just a single mobile browser instead. In this paper, we present a comprehensive web-tracking measurement study on mobile browsers and privacy-focused mobile browsers. Our study leverages a new web measurement infrastructure, OmniCrawl, which we develop to drive browsers on desktop computers and smartphones located on two continents. We capture web tracking measurements using 42 different non-emulated browsers simultaneously. We find that the third-party advertising and tracking ecosystem of mobile browsers is more similar to that of desktop browsers than previous findings suggested. We study privacy-focused browsers and find their protections differ significantly and in general are less for lower-ranked sites. Our findings also show that common methodological choices made by web measurement studies, such as the use of emulated mobile browsers and Selenium, can lead to website behavior that deviates from what actual users experience.

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Section: Tracking On Mobile Phonesmentioning

confidence: 99%

OmniCrawl: Comprehensive Measurement of Web Tracking With Real Desktop and Mobile Browsers

Cassel

Lin

Buraggina

et al. 2021

Proceedings on Privacy Enhancing Technologies

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“…In addition to the motion sensors, the imperfection in embedded Acoustic Components can be used as the device fingerprints [ 15 ]. Tiny differences can be seen among magnetic signals radiated from different CPUs [ 16 ]. Li et al [ 17 ] point out that the 3D printers can be identified by observing the differences of banding and attachment textures in different products due to the different manufacturing processes of the feeder, positioner and hot end.…”

Section: Related Workmentioning

confidence: 99%

“…The standard classification metrics including precision, recall and f1-score are used as the performance metric of Wi-Fi device identification [ 16 ]. Precision : For a certain device type, it represents the proportion of true positive samples among samples that are predicted to be positive.…”

Section: Evaluation Of Device Identificationmentioning

confidence: 99%

Probe Request Based Device Identification Attack and Defense

et al. 2020

Sensors

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Wi-Fi network has an open nature so that it needs to face greater security risks compared to wired network. The MAC address represents the unique identifier of the device, and is easily obtained by an attacker. Therefore MAC address randomization is proposed to protect the privacy of devices in a Wi-Fi network. However, implicit identifiers are used by attackers to identify user’s device, which can cause the leakage of user’s privacy. We propose device identification based on 802.11ac probe request frames. Here, a detailed analysis on the effectiveness of 802.11ac fields is given and a novel device identification method based on deep learning whose average f1-score exceeds 99% is presented. With a purpose of preventing attackers from obtaining relevant information by the device identification method above, we design a novel defense mechanism based on stream cipher. In that case, the original content of probe request frame is hidden by encrypting probe request frames and construction of probe request is reserved to avoid the finding of attackers. This defense mechanism can effectively reduce the performance of the proposed device identification method whose average f1-score is below 30%. In general, our research on attack and defense mechanism can preserve device privacy better.

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“…To avoid user operations, Sanchez-Rola designed a time-based approach, which distinguishes devices via observing the execution time of specific functions [33]. Cheng employed the magnetic induction signals emitted from the CPU module to fingerprint devices, which needs dedicated magnetic sensor [34].…”

Section: Related Workmentioning

confidence: 99%

PowerPrint: Identifying Smartphones through Power Consumption of the Battery

Chen

et al. 2020

Security and Communication Networks

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Device fingerprinting technologies are widely employed in smartphones. However, the features used in existing schemes may bring the privacy disclosure problems because of their fixed and invariable nature (such as IMEI and OS version), or the draconian of their experimental conditions may lead to a large reduction in practicality. Finding a new, secure, and effective smartphone fingerprint is, however, a surprisingly challenging task due to the restrictions on technology and mobile phone manufacturers. To tackle this challenge, we propose a battery-based fingerprinting method, named PowerPrint, which captures the feature of power consumption rather than invariable information of the battery. Furthermore, power consumption information can be easily obtained without strict conditions. We design an unsupervised learning-based algorithm to fingerprint the battery, which is stimulated with different power consumption of tasks to improve the performance. We use 15 smartphones to evaluate the performance of PowerPrint in both laboratory and public conditions. The experimental results indicate that battery fingerprint can be efficiently used to identify smartphones with low overhead. At the same time, it will not bring privacy problems, since the power consumption information is changing in real time.

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DeMiCPU: Device Fingerprinting with Magnetic Signals Radiated by CPU

Cited by 55 publications

References 16 publications

OmniCrawl: Comprehensive Measurement of Web Tracking With Real Desktop and Mobile Browsers

OmniCrawl: Comprehensive Measurement of Web Tracking With Real Desktop and Mobile Browsers

Probe Request Based Device Identification Attack and Defense

PowerPrint: Identifying Smartphones through Power Consumption of the Battery

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