Category Archives: colossus

tunny cryptanalysis before the machine age

We learn that depths provided sufficient evidence to figure the chi and psi wheel patterns – which were changing quarterly or monthly, only. And , we know that before the machine age of GCCS really got going that this change … Continue reading

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Linking Expander graphs to DES avalanche ideas

Having been away from 1950s cryptanalysis for a few weeks has helped – in the sense that we come to the material now somewhat refreshed; able to see connections that we missed. In particular, we look at expander graphs – … Continue reading

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From sequency to modern acoustic device identification–Berlin Embassy

Back at we took a look the sequency. Having created an additive signal from a set of individual weighted walsh functions taken from the hadamard matrix (or orthonormal basis functions), one learns how the inverse WHT identifies the weightings. … Continue reading

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generating minimum distance and t-resiliency, for channel reliability

it turns out useful to go re-review some of our year 3 curriculum on coding, channels, sampling, decibels etc now we that have the perspective afforded to use in studying year 4 topics. Our math is strong enough now to … Continue reading

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from fano, to turning, to bit strings to parity (and back to tunny) Suddenly realized why Turing, in On Permutations, was so focussed originally on wanting his subgroup to have terms that sum to zero. One sees, from the bit-focused example built into the fano plane, how te test for point … Continue reading

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Colossus Run–SB. Spectral Brightness

think of the overstrung piano.

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Colossus Run – SF. Spectral Flatness

spread. concentration. ration of arithmetic to geometric.

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Colossus Run–SR. Spectral Rolloff

use of argmin and an accumulator for 85% ness. Cutoff filters.

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Colossus Run–SS#2. Spectral Skewness

third moment. symmetry.

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Colossus Run–SK. Spectral Kurtosis

fourth moment. comparing to normal, with normal being 3.

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Colossus Run SI. Spectral Irregularity

jitter – variation in successive peaks.

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Colossus Run: LER. Low Energy Rate

counting softs vs louds.

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Colossus Run–ZCR. Zero Crossing Rate

measure of noise-ness – or the percussiveness of the sound field. Think spinet vs piano.

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Colossus Run–RMS. Root Mean Square

  strength, vs power or energy. Also, the most norm-ish.

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Colossus Run–TC. Tonal Centroid

interesting project of the musical 12 semitones onto a 3 donuts, corresponding to the variations in the major and minor modes to the tonic chord. Basically, major to minor detector (that is a little like the crossing zeros…)

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Colossus Run–C. Chromagram

rather than power, look at energy. Look at the 12 keys on the piano octave… Note the immunity of a particular detector (the ear) to noise, when the harmonics have the right musical quality.

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Colossus Run–SS. Spectral Spread

Building on the centroid, to note how power disperses beyond the “inner circle of the 7 point geometry).

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Colossus Run SC. Spectral Centroid

working in “power” thinking.

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Colossus Run SE. Spectral Entropy

  This is the core of the Colossus-assisted attack on Tunny, of course.

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quantum boolean algebra and colossus-era cryptanalysis

In one of this “theoretical minimum” lectures, Susskind goes through what he thinks of as elementary presentations about the linkup between boolean algebra and quantum mechanics. He is showing that there is an ordering to the evaluation of an (non-exclusive) … Continue reading

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moving back from differentials in time to simple averaging (i.e. tunny era counting)

Physics Research InternationalVolume 2010 (2010), Article ID 808424, 18 pages Research Article The Statistical Origins of Quantum Mechanics U. Klein Institut für Theoretische Physik, Johannes Kepler Universität Linz, 4040 Linz, Austria

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Klein Group, Tunny square, Lie algebra, decibannage

When we looked at the Tunny sqaure in the General Report (circa 1945), we quickly figured that it was related to the 4 –operator Klein Group. (It sticks out!…)   I see now how I need to improve the … Continue reading

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tunny 1+p and 1-p, explained

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character theory

see   instead of omega.b to a power, read b (omega) conjugated by a (the power). Turing

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Comparing des sbox design theory with Colossus-era language about proportional bulges, reliability metrics, and walsh transforms of bulge functions.

It’s fun to compare modern language of cryptanalysis with that used in the Colossus era. Perhaps the best cipher-engineering paper I’ve ever read uses that language aiming to justify how to resist DES-era cryptanalysis in a special case.  This elaboration … Continue reading

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independent component analysis

Reading the following ICA based on a Smooth Estimation of the Differential EntropyLev Faivishevsky, Jacob Goldberger, School of Engineering, Bar-Ilan University See I found myself translating. Recovering latent variables felt like “tunny wheel breaking.”  Observations of unknown linear functions … Continue reading

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relationship between Tunny delta’ing and early math for Quantum mechanics uncertainty

Lakshmi Bala, NPTEL See She does a good point of teaching what an idealistic Turing at 18 would have been learning, about Kennard’s quantum mechanics. Note how “Tunny-like” it is (with delta’ing). One even sees elements of the “algebra … Continue reading

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from Tunny-era cryptanalysis to modern American cipher, suiting NSAs mission

In the papers on war-time enigma attach methods, Turing discloses how he originally calculated odds factors. That is, for an assumed urn-style sequence of experiments (abstracting guesses to be made about unknown contents of German naval enigma bigram tables for … Continue reading

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Colossus and Tunny still helps teach cryptanalysis theory

Sometimes things click, as they did for me tonight. Something I read in the Tunny report (on how Colossus was used in the attack on Tunny ciphering machine) never made much sense – in that I could not find modern … Continue reading

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1986 limits of NSA exhaustive hypothesis testing. written 1986 – on the heyday of the pre-collapse NSA (of the early 1990s) – you can hear the self-confident tone. One gets an interesting data point. Note how the writer contrasts decoding of the code generators underlying … Continue reading

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