Although keystream reuse in stream ciphers and one time pads has been a well known problem in stream ciphers for several decades, yet the threat to real systems has still been underestimated. The keystream reuse in case of textual data has been the focus of cryptanalysts for quite some time now. In this chapter, we present the use of hidden Markov models based speech recognition approach to cryptanaly-sis of encrypted digitized speech signals in a keystream reuse situation, also known as the two time pad. We show that how an adversary can automatically recover the digitized speech signals encrypted under the same keystream provided the language (e.g. English) and digital encoding scheme (e.g. linear predictive coding) of the underlying speech signals are known. The technique is flexible enough to incorporate all modern speech coding schemes and all languages for which the speech recognition techniques exist. The technique is simple and efficient and can be practically employed with the existing HMM based probabilistic speech recognition techniques with some modification in the training (pre-computation) and/or the maximum likelihood decoding procedure. The simulation experiments showed promising initial results by recognizing around 80% correct phoneme pairs encrypted by the same keystream.
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Khan, L.A., Baig, M.S. (2009). A Hidden Markov Model based Speech Recognition Approach to Automated Cryptanalysis of Two Time Pads. In: Ao, SI., Rieger, B., Chen, SS. (eds) Advances in Computational Algorithms and Data Analysis. Lecture Notes in Electrical Engineering, vol 14. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8919-0_12
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