In this paper we consider the following problem: nodes in a MANET must disseminate data chunks using rateless codes but some nodes are assumed to be malicious, i.e., before transmitting a coded packet they may modify its payload. Nodes receiving corrupted coded packets are prevented from correctly decoding the original chunk. We propose SIEVE, a fully distributed technique to identify malicious nodes. SIEVE is based on special messages called checks that nodes periodically transmit. A check contains the list of nodes identifiers that provided coded packets of a chunk as well as a flag to signal if the chunk has been corrupted. SIEVE operates on top of an otherwise reliable architecture and it is based on the construction of a factor graph obtained from the collected checks on which an incremental belief propagation algorithm is run to compute the probability of a node being malicious. Analysis is carried out by detailed simulations using ns-3. We show that SIEVE is very accurate and discuss how nodes speed impacts on its accuracy. We also show SIEVE robustness under several attack scenarios and deceiving actions.

SIEVE: a distributed, accurate, and robust technique to identify malicious nodes in data dissemination on MANET

GAETA, Rossano;GRANGETTO, Marco;LOTI, RICCARDO
2012

Abstract

In this paper we consider the following problem: nodes in a MANET must disseminate data chunks using rateless codes but some nodes are assumed to be malicious, i.e., before transmitting a coded packet they may modify its payload. Nodes receiving corrupted coded packets are prevented from correctly decoding the original chunk. We propose SIEVE, a fully distributed technique to identify malicious nodes. SIEVE is based on special messages called checks that nodes periodically transmit. A check contains the list of nodes identifiers that provided coded packets of a chunk as well as a flag to signal if the chunk has been corrupted. SIEVE operates on top of an otherwise reliable architecture and it is based on the construction of a factor graph obtained from the collected checks on which an incremental belief propagation algorithm is run to compute the probability of a node being malicious. Analysis is carried out by detailed simulations using ns-3. We show that SIEVE is very accurate and discuss how nodes speed impacts on its accuracy. We also show SIEVE robustness under several attack scenarios and deceiving actions.
18th IEEE International Conference on Parallel and Distributed Systems (ICPADS 2012)
Singapore
17-19 Dicembre 2012
Proceedings of the 18th IEEE International Conference on Parallel and Distributed Systems (ICPADS 2012)
IEEE Computer Society Press
331
338
9781467345651
Rossano Gaeta; Marco Grangetto; Riccardo Loti
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/126440
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