Forward error correcting codes characterization based on rank properties

In this paper we deal with the error correcting code reverse engineering problem. We take the point of view of an adversary who eavesdrops communications without any a priori knowledge and who wants to recover the error correcting code parameters. For this, we propose a detailed analysis of the central criterion which is the origin of all state of the art reconstruction techniques. This criterion, also called the rank criterion, consists in observing the variation of the rank of a particular matrix built with the intercepted bit stream to deduce the parameters of the code. We also propose new equations to reconstruct turbo codes. Our analysis is confirmed by experimental results.

[1]  Johann Barbier,et al.  Reconstruction of turbo-code encoders , 2005, SPIE Defense + Commercial Sensing.

[2]  R. Brualdi,et al.  Handbook Of Coding Theory , 2011 .

[3]  Johann Barbier Analyse de canaux de communication dans un contexte non coopératif : application aux codes correcteurs d'erreurs et à la stéganalyse , 2007 .

[4]  Anne Canteaut,et al.  A New Algorithm for Finding Minimum-Weight Words in a Linear Code: Application to McEliece’s Cryptosystem and to Narrow-Sense BCH Codes of Length , 1998 .

[5]  Eric Filiol Reconstruction of Convolutional Encoders over GF(q) , 1997, IMACC.

[6]  G. Burel,et al.  Blind Estimation of Encoder and Interleaver Characteristics in a Non Cooperative Context , 2003 .

[7]  Igor N. Kovalenko,et al.  Distribution of the Linear Rank of a Random Matrix , 1973 .

[8]  Mathieu Cluzeau,et al.  Block code reconstruction using iterative decoding techniques , 2006, 2006 IEEE International Symposium on Information Theory.

[9]  Sébastien Houcke,et al.  Blind detection of interleaver parameters , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[10]  Roland Gautier,et al.  Blind recovery of the second convolutional encoder of a turbo-code when its systematic outputs are punctured , 2008 .

[11]  Nicolas Sendrier,et al.  Reconstruction of convolutional codes from noisy observation , 2009, 2009 IEEE International Symposium on Information Theory.

[12]  Matthieu Finiasz,et al.  Reconstruction of punctured convolutional codes , 2009, 2009 IEEE Information Theory Workshop.

[13]  Antoine Valembois,et al.  Detection and recognition of a binary linear code , 2001, Discret. Appl. Math..

[14]  Sebastien Houcke,et al.  Algebraic Approach for the Reconstruction of Linear and Convolutional Error Correcting Codes , 2008 .