Distributed Detection in the Presence of

Distributed detection in the presence of cooperative (Byzantine)attackisconsidered.Itisassumedthatafractionofthe monitoring sensors are compromised by an adversary, and these compromised (Byzantine) sensors are reprogrammed to transmit fictitiousobservationsaimedat confusing thedecisionmakeratthe fusion center. For detection under binary hypotheses with quan- tized sensor observations, the optimal attacking distributions for Byzantine sensors that minimize the detection error exponent are obtained using a "water-filling" procedure. The smallest error ex- ponent, as a function of the Byzantine sensor population, charac- terizes the power of attack. Also obtained is the minimum fraction of Byzantine sensors that destroys the consistency of detection at the fusion center. The case when multiple measurements are made at the remote nodes is also considered, and it is shown that the de- tection performance scales with the number of sensors differently from the number of observations at each sensor. Index Terms—Byzantine attack, distributed detection, network defense.

[1]  Yunghsiang Sam Han,et al.  A witness-based approach for data fusion assurance in wireless sensor networks , 2003, GLOBECOM '03. IEEE Global Telecommunications Conference (IEEE Cat. No.03CH37489).

[2]  Leslie Lamport,et al.  The Byzantine Generals Problem , 1982, TOPL.

[3]  Zhi-Quan Luo,et al.  Distributed signal processing in sensor networks , 2006 .

[4]  Haiyun Luo,et al.  Statistical en-route filtering of injected false data in sensor networks , 2005, IEEE J. Sel. Areas Commun..

[5]  Pramod K. Varshney,et al.  Distributed Detection and Data Fusion , 1996 .

[6]  Rick S. Blum,et al.  The good, bad and ugly: distributed detection of a known signal in dependent Gaussian noise , 2000, IEEE Trans. Signal Process..

[7]  Ming Dong,et al.  On distributed fault-tolerant detection in wireless sensor networks , 2006, IEEE Transactions on Computers.

[8]  Danny Dolev,et al.  The Byzantine Generals Strike Again , 1981, J. Algorithms.

[9]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[10]  P. J. Huber A Robust Version of the Probability Ratio Test , 1965 .

[11]  Rick S. Blum,et al.  Distributed detection with multiple sensors I. Advanced topics , 1997, Proc. IEEE.

[12]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[13]  Tracey Ho,et al.  Byzantine modification detection in multicast networks using randomized network coding , 2004, International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings..

[14]  Pramod K. Varshney,et al.  Distributed detection with multiple sensors I. Fundamentals , 1997, Proc. IEEE.

[15]  Elaine Shi,et al.  Designing secure sensor networks , 2004, IEEE Wireless Communications.

[16]  Parameswaran Ramanathan,et al.  Fault tolerance in collaborative sensor networks for target detection , 2004, IEEE Transactions on Computers.

[17]  Lang Tong,et al.  Distributed Source Coding in the Presence of Byzantine Sensors , 2007, IEEE Transactions on Information Theory.