Comment on “Remote Physical Device Fingerprinting”

Polčák, Libor; Jirasek, Jakub; Matoušek, Petr

doi:10.1109/tdsc.2013.26

Cited by 18 publications

(5 citation statements)

References 3 publications

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“…en, Polcak validated Kohno's technology again in 2014, which shows that the method is suitable for the computer identification [32]. Identifying a device through these hardware does have a good performance.…”

Section: Related Workmentioning

confidence: 93%

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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Section: Related Workmentioning

confidence: 93%

PowerPrint: Identifying Smartphones through Power Consumption of the Battery

Chen

et al. 2020

Security and Communication Networks

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“…To the best of our knowledge, we are the first to report on the privacy aspects of the TFO protocol. While research work on privacy issues within TCP that allows distinguishing clients or servers based on their TCP timestamps [17,22] exists, our reported storage-based tracking mechanism is unrelated to such tracking approaches via the TCP timestamps. A similar tracking mechanism has been reported for the QUIC transport protocol [30].…”

Section: Related Workmentioning

confidence: 99%

Enhanced Performance and Privacy for TLS over TCP Fast Open

Mueller

Burkert

et al. 2020

Proceedings on Privacy Enhancing Technologies

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Small TCP flows make up the majority of web flows. For them, the TCP three-way handshake induces significant delay overhead. The TCP Fast Open (TFO) protocol can significantly decrease this delay via zero round-trip time (0-RTT) handshakes for all TCP handshakes that follow a full initial handshake to the same host. However, this comes at the cost of privacy limitations and also has some performance limitations. In this paper, we investigate the TFP deployment on popular websites and browsers. We found that a client revisiting a web site for the first time fails to use an abbreviated TFO handshake in 40% of all cases due to web server load-balancing using multiple IP addresses. Our analysis further reveals significant privacy problems of the protocol design and implementation. Network-based attackers and online trackers can exploit TFO to track the online activities of users. As a countermeasure, we introduce a novel protocol called TCP Fast Open Privacy (FOP). TCP FOP prevents tracking by network attackers and impedes third-party tracking, while still allowing 0-RTT handshakes as in TFO. As a proof-of-concept, we have implemented the proposed protocol for the Linux kernel and a TLS library. Our measurements indicate that TCP FOP outperforms TLS over TFO when websites are served from multiple IP addresses.

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“…In the field of network traffic analysis, researchers devote to extracting attributes from network traffic to identify the device or users even though the device IP changes. In the physical layer, Polcak et al [17] proposed a kind of attribute to identify computers by their clock skew computed from TCP timestamps. In the operating system layer, Franklin and McCoy [18] found that the 802.11 probe request time interval can reveal the active scanning algorithms employed by wireless derivers.…”

Section: Related Workmentioning

confidence: 99%

A Novel IM Sync Message-Based Cross-Device Tracking

Guo

Luo

Ling

et al. 2020

Security and Communication Networks

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Cybercrime is significantly growing as the development of internet technology. To mitigate this issue, the law enforcement adopts network surveillance technology to track a suspect and derive the online profile. However, the traditional network surveillance using the single-device tracking method can only acquire part of a suspect’s online activities. With the emergence of different types of devices (e.g., personal computers, mobile phones, and smart wearable devices) in the mobile edge computing (MEC) environment, one suspect can employ multiple devices to launch a cybercrime. In this paper, we investigate a novel cross-device tracking approach which is able to correlate one suspect’s different devices so as to help the law enforcement monitor a suspect’s online activities more comprehensively. Our approach is based on the network traffic analysis of instant messaging (IM) applications, which are typical commercial service providers (CSPs) in the MEC environment. We notice a new habit of using IM applications, that is, one individual logs in the same account on multiple devices. This habit brings about devices’ receiving sync messages, which can be utilized to correlate devices. We choose five popular apps (i.e., WhatsApp, Facebook Messenger, WeChat, QQ, and Skype) to prove our approach’s effectiveness. The experimental results show that our approach can identify IM messages with high F1-scores (e.g., QQ’s PC message is 0.966, and QQ’s phone message is 0.924) and achieve an average correlating accuracy of 89.58% of five apps in an 8-people experiment, with the fastest correlation speed achieved in 100 s.

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Comment on “Remote Physical Device Fingerprinting”

Abstract: On the Use of the ISBAS Acronym in InSAR Applications. Comment on Vajedian, S.; Motagh, M.; Nilfouroushan, F. StaMPS Improvement for Deformation Analysis in Mountainous Regions: Implications for the Damavand Volcano and Mosha Fault in Alborz.

Cited by 18 publications

References 3 publications

PowerPrint: Identifying Smartphones through Power Consumption of the Battery

PowerPrint: Identifying Smartphones through Power Consumption of the Battery

Enhanced Performance and Privacy for TLS over TCP Fast Open

A Novel IM Sync Message-Based Cross-Device Tracking

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