Big bias hunting in Amazonia: Large-scale computation and exploitation of RC4 biases (invited paper)

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Abstract

RC4 is (still) a very widely-used stream cipher. Previous work by AlFardan et al. (USENIX Security 2013) and Paterson et al. (FSE 2014) exploited the presence of biases in the RC4 keystreams to mount plaintext recovery attacks against TLS-RC4 and WPA/TKIP. We improve on the latter work by performing large-scale computations to obtain accurate estimates of the single-byte and double-byte distributions in the early portions of RC4 keystreams for the WPA/TKIP context and by then using these distributions in a novel variant of the previous plaintext recovery attacks. The distribution computations were conducted using the Amazon EC2 cloud computing infrastructure and involved the coordination of 213 hyper-threaded cores running in parallel over a period of several days. We report on our experiences of computing at this scale using commercial cloud services. We also study Microsoft’s Point-to-Point Encryption protocol and its use of RC4, showing that it is also vulnerable to our attack techniques.

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APA

Paterson, K. G., Poettering, B., & Schuldt, J. C. N. (2014). Big bias hunting in Amazonia: Large-scale computation and exploitation of RC4 biases (invited paper). In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8873, pp. 398–419). Springer Verlag. https://doi.org/10.1007/978-3-662-45611-8_21

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