Clock Skew Based Client Device Identification in Cloud Environments

Abstract

Along with the growth of cloud computing and mobile devices, the importance of client device identity concern over cloud environment is emerging. To provide a lightweight yet reliable method for device identification, an application layer approach based on clock skew fingerprint is proposed. The developed experimental platform adapts AJAX technology to collect the timestamps of client devices in the cloud server during connection time, then calculate the clock skews of client devices. Few methods based on linear regression and piecewise minimum algorithm are developed to optimize the precision and shorten timestamp collection process. A jump point detection scheme is also proposed to resolve the offset drifting problem, which is usually caused by switching network or temporary disconnection. Finally, two experiments are conducted to study the effectiveness of clock skew fingerprint, and the results illustrate that the false positive rate and the false negative rate, in the worst case, are both no more than 8% when the tolerance threshold is set appropriately.

Design of client device identification by clock skew in clouds

“…However, as the result of piecewise minimum algorithm is not a straight line, we can not use it directly to approach one estimated skew value. Huang et al recently proposed a quick piecewise minimum algorithm (QPM) [9] to fit this requirement. QPM uses only minimum delays of the first segment and the last segment.…”

“…The goal of authentication servers is to verify the identity of users. When receiving the messages from web application, authentication server requests the corresponding personal data from the database server and exploits the identification technique mentioned in [9]. If the identity is valid, the authentication server grants the access of the web application.…”

“…According to these two properties, clock skew can be regarded as a fingerprint of any device with digital clock. Currently, Huang et al have confirmed that clock skew, the difference of clocking speeds between two clocks, can be used as a practical tool to verify client device identity in cloud environment [9]. They assume that the cloud server maintains a registered list of physical devices that are associated with a collection of valid users.…”

Hough Transform-Based Clock Skew Measurement by Dynamically Locating the Region of Offset Majority

“…It is clear that both methods fail to give a correct estimation. It has also been reported that this kind of segmentation phenomena occurs when the route changes due to, for example, the sending device switching from a wired connection to a wireless one, or changing its mobile network relay base station [9]. Figure 2 (b) illustrates a somewhat overdone example.…”

“…As the widespread use of clock skew now ranges from cases of notebooks communicating inside a WLAN [4], [5] to smart phone applications accessing cloud services [7], [9], there is significant demand for an approach able to provide accurate estimations even to non-classical offset distributions. A well-known non-classical case is caused by time synchronization, e.g.…”