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The popularity of sensor networks and their many uses in critical domains such as military and healthcare make them more vulnerable to malicious attacks. In such contexts, trustworthiness of sensor data and their provenance is critical for decision-making. In this demonstration, we present an efficient and secure approach for transmitting provenance information about sensor data. Our provenance approach uses light-weight in-packet Bloom filters that are encoded as sensor data travels through intermediate sensor nodes, and are decoded and verified at the base station. Our provenance technique is also able to defend against malicious attacks such as packet dropping and allows one to detect the responsible node for packet drops. As such it makes possible to modify the transmission route to avoid nodes that could be compromised or malfunctioning. Our technique is designed to create a trustworthy environment for sensor nodes where only trusted data is processed.

Fingerprints Tor networkHidden services Timestamps a b s t r a c t Hidden services are anonymously hosted services that can be accessed over an anonymity network, such as Tor. While most hidden services are legitimate, some host illegal content.There has been a fair amount of research on locating hidden services, but an open problem is to develop a general method to prove that a physical machine, once confiscated, was in fact the machine that had been hosting the illegal content. In this paper we assume that the hidden service logs requests with some timestamp, and give experimental results for leaving an identifiable fingerprint in this log file as a timing channel that can be recovered from the timestamps. In 60 min, we are able to leave a 36-bit fingerprint that can be reliably recovered. The main challenges are the packet delays caused by the anonymity network that requests are sent over and the existing traffic in the log from the actual clients accessing the service. We give data to characterize these noise sources and then describe an implementation of timing-channel fingerprinting for an Apache web server based hidden service on the Tor network, where the fingerprint is an additive channel that is superencoded with a ReedeSolomon code for reliable recovery. Finally, we discuss the inherent tradeoffs and possible approaches to making the fingerprint more stealthy.

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In a wireless sensor networks, packet losses can result from attacks affecting the nodes or the wireless links connecting the nodes. Failure to identify the actual attack can undermine the efficacy of the attack responses. We thus need approaches to correctly identify the cause of packet losses. In this poster paper, we address this problem by proposing and building a fine-grained analysis (FGA) tool that investigates the causes of packet losses and reports the most likely cause of these losses. Our tool uses parameters, e.g. RSSI and LQI, transmitted with every received packet to profile the links between nodes and their corresponding neighborhood. Through real-world experiments, we have validated our approach and shown that our tool is able to differentiate between the various attacks that may affect the nodes and the links.

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