Early and Lightweight Distributed Detection of Node Replication Attack in Sensor Networks

Tran, Thanh Dai; Agbinya, Johnson I.

doi:10.1109/wcnc.2010.5506482

Cited by 6 publications

(1 citation statement)

References 12 publications

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“…Some other solutions have been proposed. Among the most recent ones, we could cite [12] where the basic idea is to integrate a constant countervalue into a HELLO message broadcasted by each sensor node after its deployment. The counter is maintained by the node to keep track.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Node Replication Attacks Detection in Wireless Sensor Networks

Znaïdi

Minier

Ubéda

2013

International Journal of Distributed Sensor Networks

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Wireless sensor networks (WSNs) are composed of numerous low-cost, low-power sensor nodes communicating at short distance through wireless links. Sensors are densely deployed to collect and transmit data of the physical world to one or few destinations called the sinks. Because of open deployment in hostile environment and the use of low-cost materials, powerful adversaries could capture them to extract sensitive information (encryption keys, identities, addresses, etc.). When nodes may be compromised, "beyond cryptography" algorithmic solutions must be envisaged to complement the cryptographic solutions. This paper addresses the problem of nodes replication; that is, an adversary captures one or several nodes and inserts duplicated nodes at any location in the network. If no specific detection mechanisms are established, the attacker could lead many insidious attacks. In this work, we first introduce a new hierarchical distributed algorithm for detecting node replication attacks using a Bloom filter mechanism and a cluster head selection (see also Znaidi et al. (2009)). We present a theoretical discussion on the bounds of our algorithm. We also perform extensive simulations of our algorithm for random topologies, and we compare those results with other proposals of the literature. Finally, we show the effectiveness of our algorithm and its energy efficiency.

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Section: Related Workmentioning

confidence: 99%