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In the process, we collected crypto exploit code in dozens of different languages, ranging from X86 assembly to Haskell.

With the permission of the participants, we've built a "Rosetta Code" site with per-language implementations of each of the crypto attacks we taught.

As part of the algorithm description I will walk through a Python machine learning library that we will be releasing in the conference material which allows users to detect feature frequencies over billions of items on commodity hardware.

To make this algorithm scale, we use an approximate feature counting technique and a feature-hashing trick drawn from the machine-learning domain, allowing for the fast feature extraction and fast retrieval of sample "near neighbors" even when handling millions of binaries.Our algorithm was developed over the course of three years and has been evaluated both internally and by an independent test team at MIT Lincoln Laboratories: we scored the highest on these tests against four competing malware cluster recognition techniques and we believe this was because of our unique "ensemble" approach.In the presentation, I will give details on how to implement the algorithm and will go over these algorithm results in a series of large-scale interactive malware visualizations.If we could recover this shared-code network, we could provide much needed context for and insight into newly observed malware.

For example, our analysis could leverage previous reverse engineering work performed on a new malware sample's older "relatives," giving important context and accelerating the reverse engineering process.

IEEE 802.1x has been leveraged for a long time for authentication purposes.