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.

SUMMARYThis work aims to address the security vulnerability of the Flooding Time Synchronization Protocol (FTSP), which is currently one of the most popular approaches for time synchronization in wireless sensor networks. FTSP has advanced features, such as implicitly dynamic topology and high time accuracy, but still has unresolved security issues. In order to defend against attacks from malicious nodes, we propose several technologies to reinforce the structure of FTSP. First, a reference node selecting mechanism is proposed to reduce the effect of multiple reference nodes, and four filters are proposed to defend against seqNum attack, global time attack and node replication attack. Experiment results show that the proposed sequence number blacklist filter and the global time blacklist filter are effective in defending against the aforementioned attacks. Second, a new root selection mechanism is proposed to secure the process of updating the root node. Combining the root selection mechanism with the global time black list filter, the proposed mechanisms successfully defend against traitor attacks on FTSP in our experiment. Copyright © 2013 John Wiley & Sons, Ltd.

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This paper introduces a scheme of client device identification by clock skew in cloud environments. Clock skew of a remote device is considered an effective physical characteristic and is suitable for device identification purposes. As the temperature is always steady inside data center, we utilize this advantage to verify that two arithmetic relations hold with negligible measurement error between servers and develop a fault-tolerant multi-server skew measuring scheme. We have also implemented a lightweight application layer experimental platform to provide the device identification and account authentication service in an implicit manner based on the clock skew fingerprinting technique. Two experiments are conducted to confirm the correctness of skew arithmetic, and to examine the feasibility of client device identification by clock skew respectively. The experiment results show that the maximum error of additive inverse is only 0.01 ppm and the linearity of clock skew holds with error no more then 0.03 ppm while utilizing physical hardware. Furthermore, the capability of load balance and fault tolerance achieved by multiple time servers are also demonstrated in the experiments.