Detecting Sybil attacks in Wireless Sensor Networks using neighboring information
Computer Networks: The International Journal of Computer and Telecommunications Networking
Random-walk based approach to detect clone attacks in wireless sensor networks
IEEE Journal on Selected Areas in Communications
From time domain to space domain: detecting replica attacks in mobile ad hoc networks
INFOCOM'10 Proceedings of the 29th conference on Information communications
ACS'11 Proceedings of the 11th WSEAS international conference on Applied computer science
Review: Detecting node replication attacks in wireless sensor networks: A survey
Journal of Network and Computer Applications
Time-bounded essential localization for wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Scrutinising well-known countermeasures against clone node attack in mobile wireless sensor networks
International Journal of Grid and Utility Computing
A trustworthiness evaluation method for wireless sensor nodes based on d-s evidence theory
WASA'13 Proceedings of the 8th international conference on Wireless Algorithms, Systems, and Applications
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A central problem in sensor network security is that sensors are susceptible to physical capture attacks. Once a sensor is compromised, the adversary can easily launch {\it clone attacks} by replicating the compromised node, distributing the clones throughout the network, and starting a variety of insider attacks. Previous works against clone attacks suffer from either a high communication/storage overhead or a poor detection accuracy. In this paper, we propose a novel scheme for detecting clone attacks in sensor networks, which computes for each sensor a social fingerprint by extracting the neighborhood characteristics, and verifies the legitimacy of the originator for each message by checking the enclosed fingerprint. The fingerprint generation is based on the super imposed $s$-disjunct code, which incurs a very light communication and computation overhead. The fingerprint verification is conducted at both the base station and the neighboring sensors, which ensures a high detection probability. The security and performance analysis indicate that our algorithm can identify clone attacks with a high detection probability at the cost of a low computation/communication/storage overhead. To our best knowledge, our scheme is the first to provide realtime detection of clone attacks in an effective and efficient way.