On the Robustness of Mobile Device Fingerprinting

Hupperich, Thomas; Maiorca, Davide; Kührer, Marc; Holz, Thorsten; Giacinto, Giorgio

doi:10.1145/2818000.2818032

Cited by 43 publications

(3 citation statements)

References 11 publications

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“…Additionally, researchers have performed large crawls of the web that confirm a steady growth of browser fingerprinting [1], [2], [9], [22]. While most of these studies focused on desktops, others demonstrated they could successfully fingerprint mobile device browsers [11], [14]. Finally, a study we conducted in 2016 confirmed Eckersley's findings, but observed a notable shift in some attributes [14].…”

Section: Background and Motivationsmentioning

confidence: 58%

FP-STALKER: Tracking Browser Fingerprint Evolutions

Vastel

Laperdrix

Rudametkin

et al. 2018

2018 IEEE Symposium on Security and Privacy (SP)

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Abstract-Browser fingerprinting has emerged as a technique to track users without their consent. Unlike cookies, fingerprinting is a stateless technique that does not store any information on devices, but instead exploits unique combinations of attributes handed over freely by browsers. The uniqueness of fingerprints allows them to be used for identification. However, browser fingerprints change over time and the effectiveness of tracking users over longer durations has not been properly addressed.In this paper, we show that browser fingerprints tend to change frequently-from every few hours to days-due to, for example, software updates or configuration changes. Yet, despite these frequent changes, we show that browser fingerprints can still be linked, thus enabling long-term tracking.FP-STALKER is an approach to link browser fingerprint evolutions. It compares fingerprints to determine if they originate from the same browser. We created two variants of FP-STALKER, a rule-based variant that is faster, and a hybrid variant that exploits machine learning to boost accuracy. To evaluate FP-STALKER, we conduct an empirical study using 98, 598 fingerprints we collected from 1, 905 distinct browser instances. We compare our algorithm with the state of the art and show that, on average, we can track browsers for 54.48 days, and 26 % of browsers can be tracked for more than 100 days.

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Section: Background and Motivationsmentioning

confidence: 58%

FP-STALKER: Tracking Browser Fingerprint Evolutions

Vastel

Laperdrix

Rudametkin

et al. 2018

2018 IEEE Symposium on Security and Privacy (SP)

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“…The authors Hupperich et al (2015) also identify five strategies that can be used to detect possible fraud or cyber-attacks over the fingerprint procedure:…”

Section: Related Workmentioning

confidence: 99%

A Multi-layer Approach for Mobile Devices: Behavioral Fingerprinting

Apostol¹,

Mocanu²,

Mocanu³

et al. 2023

SIC

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In distributed systems such as mobile networks, problems like heterogeneity and real-time reliable responses represent constant challenges. Furthermore, reliable identification of the participants in mobile networks is a challenging research problem because of the susceptibility to security attacks. Also, reliable identification of participants means to be able to distinguish between the device and its user. In this paper, a multi-layer mechanism is proposed for reliable identification of devices and their users in mobile networks, based on the smartphone sensor data. The proposed model can uniquely identify both the device and the users by leveraging a mobile application that harvests mobile sensor data in various usage scenarios and analyse different usage patterns. Moreover, in order to achieve data confidentiality, a cryptographic scheme is integrated, based on the public key exchange mechanism. The obtained results show that the model proposed in this paper provides meaningful insights about user behavioral patterns, without special permissions and without disclosing the actual owner identity or biometric data.

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“…Device fingerprinting is very popular in internet-connected general purpose computing devices to track user behaviour and application usage. Some of the interesting applications include browser fingerprinting for web analytics, user tracking, fraud detection and accountability [9,23,27] and have gained a significant interests from cyber-security community. While it is clear that device fingerprinting can bring a lot of benefits -especially for providing automated and customisable user experience -there are also concerns that it can pose security and privacy risks [29,39].…”

Section: Introductionmentioning

confidence: 99%

Position paper

Yadav

Feraudo

Arief

et al. 2020

Proceedings of the 2nd International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Thi

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The popularity of the Internet of Things (IoT) devices makes it increasingly important to be able to fingerprint them, for example in order to detect if there are misbehaving or even malicious IoT devices in one's network. However, there are many challenges faced in the task of fingerprinting IoT devices, mainly due to the huge variety of the devices involved. At the same time, the task can potentially be improved by applying machine learning techniques for better accuracy and efficiency. The aim of this paper is to provide a systematic categorisation of machine learning augmented techniques that can be used for fingerprinting IoT devices. This can serve as a baseline for comparing various IoT fingerprinting mechanisms, so that network administrators can choose one or more mechanisms that are appropriate for monitoring and maintaining their network. We carried out an extensive literature review of existing papers on fingerprinting IoT devices-paying close attention to those with machine learning features. This is followed by an extraction of important and comparable features among the mechanisms outlined in those papers. As a result, we came up with a key set of terminologies that are relevant both in the fingerprinting context and in the IoT domain. This enabled us to construct a framework called IDWork, which can be used for categorising existing IoT fingerprinting mechanisms in a way that will facilitate a coherent and fair comparison of these mechanisms. We found that the majority of the IoT fingerprinting mechanisms take a passive approach-mainly through network sniffing-instead of being intrusive and interactive with the device of interest. Additionally, a significant number of the surveyed mechanisms employ both static and dynamic approaches, in order to benefit from complementary features that can be more robust against certain attacks such as spoofing and replay attacks. CCS CONCEPTS • Security and privacy → Biometrics; • Networks → Layering; • Computer systems organization → Sensor networks; • Computing methodologies → Machine learning approaches; Modeling methodologies; • General and reference → Measurement.

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On the Robustness of Mobile Device Fingerprinting

Cited by 43 publications

References 11 publications

FP-STALKER: Tracking Browser Fingerprint Evolutions

FP-STALKER: Tracking Browser Fingerprint Evolutions

A Multi-layer Approach for Mobile Devices: Behavioral Fingerprinting

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