Secure Data Aggregation Technique for Wireless Sensor Networks in the Presence of Collusion Attacks
Abstract— Secure Data Aggregation Technique for Wireless Sensor Networks in the Presence of Collusion Attacks. Due 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 < Final Year Projects 2016 > 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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