Leveraging Sensor Fingerprinting for Mobile Device Authentication

Hupperich, Thomas; Hosseini, Henry; Holz, Thorsten

doi:10.1007/978-3-319-40667-1_19

Cited by 16 publications

(5 citation statements)

References 19 publications

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“…Similarly, such imperfections also affect the microphones and speakers [132], [133], which also allow to fingerprint devices. In addition, by combining multiple sensors, even higher accuracies can be achieved [134].…”

Section: A Passive Attacksmentioning

confidence: 99%

Systematic Classification of Side-Channel Attacks: A Case Study for Mobile Devices

Spreitzer

Moonsamy

Korak

et al. 2018

IEEE Commun. Surv. Tutorials

187

109

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Abstract-Side-channel attacks on mobile devices have gained increasing attention since their introduction in 2007. While traditional side-channel attacks, such as power analysis attacks and electromagnetic analysis attacks, required physical presence of the attacker as well as expensive equipment, an (unprivileged) application is all it takes to exploit the leaking information on modern mobile devices. Given the vast amount of sensitive information that are stored on smartphones, the ramifications of side-channel attacks affect both the security and privacy of users and their devices.In this paper, we propose a new categorization system for sidechannel attacks, which is necessary as side-channel attacks have evolved significantly since their scientific investigations during the smart card era in the 1990s. Our proposed classification system allows to analyze side-channel attacks systematically, and facilitates the development of novel countermeasures. Besides this new categorization system, the extensive survey of existing attacks and attack strategies provides valuable insights into the evolving field of side-channel attacks, especially when focusing on mobile devices. We conclude by discussing open issues and challenges in this context and outline possible future research directions.

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Section: A Passive Attacksmentioning

confidence: 99%

Systematic Classification of Side-Channel Attacks: A Case Study for Mobile Devices

Spreitzer

Moonsamy

Korak

et al. 2018

IEEE Commun. Surv. Tutorials

187

109

View full text Add to dashboard Cite

show abstract

“…The X, Y, and Z axis conversion form of the mobile phone constructed in the two placement states shown in Figure 9 from the reference coordinate system is obtained by formulas ( 9), (10), (11). Here we take the Huawei mate20 mobile phone as an example.…”

Section: E Attitude-consistency Of Sensor Fingerprintmentioning

confidence: 99%

Mobile Device Fingerprint Identification Using Gyroscope Resonance

Tian

Zhang²,

et al. 2021

IEEE Access

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Device fingerprint is information about the target computing device for the purpose of identification. The fingerprinting algorithm that how to assimilate the target into an identifier has been well studied for more than 20 years. However, the recent obfuscation method against device fingerprinting makes collecting this information more difficult. In order to solve these problems, this paper proposes a new type of fingerprinting method that relies on the resonant frequency response of the gyroscope. Our method first generates an ultrasonic as the trigger signal and obtains the sensor's output through the application program or the web browser as the response. After the sensor output being normalized during the data preprocessing, resonance features are extracted after frequency domain analysis. Finally, the mobile device is identified through these features matching. In the experiment, we test many types of mobile phones to demonstrate that our method is feasible while users are unaware of this process. For instance, we find that some features are stable when the posture of the mobile device or the time change. There are 10 features based on resonance frequency that can help us improve the classification accuracy to 96.5%. The comprehensive experiments demonstrate that our method can effectively distinguish different types of mobile devices. Even if it is the same model of mobile devices with the same type of gyroscopes, this method still has a good performance.

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“…In [8] and [9], the authors set up three background audios (silent, 20khz sine wave, pop music) to explore whether the sensor fingerprint is affected by the background audio, and the experimental results showed that background audio will affect the performance of sensor fingerprint. Hupperich et al [10] collects various sensor data such as accelerometers, gyroscopes, magnetometers and so on in more than 5000 devices. Their experiments showed that it was difficult to identify the device accurately with such a large amount of data.…”

Section: Related Workmentioning

confidence: 99%

Accelerometer-Based Mobile Device Identification System for the Realistic Environment

Ding

Min

2019

IEEE Access

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With the rapid development of the Internet and mobile technology, the frequency of online fraud incidents continues to increase, and the fraudulent means are increasingly diverse. Traditional antifraud methods can no longer effectively deal with the serious security situation at present because of their own various shortcomings. In this paper, a device identification system based on acceleration sensor is constructed which takes into consideration the imperfections of the sensor in the manufacturing process and assembley process. The system can identify the user's device by taking advantages of the data collected in a flat static state, and thus confirm the user's identity. The acceleration data of the device is first collected, and then the data in flat static state is extracted through the state discrimination algorithm. After the feature extraction process, a 20-dimensional feature vector is obtained. Finally, a device identification model combining one-class classifier and multi-class classifier is trained. The model can identify unknown devices and classify known devices. The method proposed in this paper solves the problem that the device state cannot be determined in the real world. Further on we propose a multi-layer device identification model that can identify unknown device without training threshold. Compared with the previous studies in the literature, the biggest innovation of our method is to consider the user authentication of device fingerprint in the real world.

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Leveraging Sensor Fingerprinting for Mobile Device Authentication

Cited by 16 publications

References 19 publications

Systematic Classification of Side-Channel Attacks: A Case Study for Mobile Devices

Systematic Classification of Side-Channel Attacks: A Case Study for Mobile Devices

Mobile Device Fingerprint Identification Using Gyroscope Resonance

Accelerometer-Based Mobile Device Identification System for the Realistic Environment

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