IEEE Transactions on Dependable and Secure Computing

IEEE Transactions on Dependable and Secure Computing (TDSC) is a bimonthly journal that publishes archival research results focusing on foundations, methodologies, and mechanisms that support the achievement—through design, modeling, and evaluation—of systems and networks that are dependable and secure to the desired degree without compromising performance. Read the full scope of TDSC

IEEE Transactions on Dependable and Secure Computing (TDSC) has moved to the OnlinePlus publication model.

From the January/February 2015 issue

Secure Data Aggregation Technique for Wireless Sensor Networks in the Presence of Collusion Attacks

By Mohsen Rezvani, Aleksandar Ignjatovic, Elisa Bertino, and Sanjay Jha

Featured article thumbnail imageDue to limited computational power and energy resources, aggregation of data from multiple sensor nodes done at the aggregating node is usually accomplished by simple methods such as averaging. However such aggregation is known to be highly vulnerable to node compromising attacks. Since WSN are usually unattended and without tamper resistant hardware, they are highly susceptible to such attacks. Thus, ascertaining trustworthiness of data and reputation of sensor nodes is crucial for WSN. As the performance of very low power processors dramatically improves, future aggregator nodes will be capable of performing more sophisticated data aggregation algorithms, thus making WSN less vulnerable. Iterative filtering algorithms hold great promise for such a purpose. Such algorithms simultaneously aggregate data from multiple sources and provide trust assessment of these sources, usually in a form of corresponding weight factors assigned to data provided by each source. In this paper we demonstrate that several existing iterative filtering algorithms, while significantly more robust against collusion attacks than the simple averaging methods, are nevertheless susceptive to a novel sophisticated collusion attack we introduce. To address this security issue, we propose an improvement for iterative filtering techniques by providing an initial approximation for such algorithms which makes them not only collusion robust, but also more accurate and faster converging.

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