KiHoLee
138897aa8d
The package was missing every script behind the KM (lrn.) curves and both learned table rows: exp_learned's driver, the merge that folds the learned rows into sec_compare.csv and refresh_summary.csv, and the report that reads the learned numbers back. It was also missing sec_keylen_perm.csv, so Fig. 3 could not be regenerated at all, and the two diagnostics that answer why a fixed key beats a learned one here and where a learned mask would win instead. The learned artifacts themselves are regenerated. They were trained on the cross-entropy alone, which drifts to disjoint sparse supports: 99 percent of each key's energy on about six of the 64 entries, so a digit is decided over a sixth of its period and the key set is a choice of support rather than a dense direction in R^L. They are now the regularized keys of Section V-C, and check_consistency asserts which of the two families the figures draw. Verified from inside this repository: replot_security.py rebuilds all seven result figures, make_tables.py reproduces both result tables, and check_consistency.py passes every check that does not need the manuscript. The README now lists what ships. Its run list, layout and figure map had none of the learned pipeline, named two tables the manuscript renders as prose, and gave Fig. 3 no data file for its permutation curve.
1.2 KiB
1.2 KiB
| 1 | snr_db | n_frames | kappa | eve_ser |
|---|---|---|---|---|
| 2 | 0 | 1 | 0.05034990469 | 0.997911625 |
| 3 | 0 | 2 | 0.05894429332 | 0.662401 |
| 4 | 0 | 3 | 0.08379636761 | 0.37693875 |
| 5 | 0 | 4 | 0.107222241 | 0.22233425 |
| 6 | 0 | 5 | 0.1292011227 | 0.152770125 |
| 7 | 0 | 6 | 0.1429406652 | 0.131741625 |
| 8 | 0 | 8 | 0.1788643047 | 0.086013875 |
| 9 | 0 | 10 | 0.2027079877 | 0.077310875 |
| 10 | 0 | 12 | 0.2198284302 | 0.07391475 |
| 11 | 0 | 16 | 0.2579282403 | 0.069537375 |
| 12 | 0 | 24 | 0.3094305053 | 0.066363625 |
| 13 | 0 | 32 | 0.3459532984 | 0.06496575 |
| 14 | 0 | 48 | 0.4114221439 | 0.063700375 |
| 15 | 0 | 64 | 0.4645803586 | 0.06309375 |
| 16 | 10 | 1 | 0.05442041112 | 0.950243625 |
| 17 | 10 | 2 | 0.1407177548 | 0.181911125 |
| 18 | 10 | 3 | 0.2204171771 | 0.080573875 |
| 19 | 10 | 4 | 0.2837046772 | 0.072395125 |
| 20 | 10 | 5 | 0.3326446027 | 0.06649075 |
| 21 | 10 | 6 | 0.3704789877 | 0.0648705 |
| 22 | 10 | 8 | 0.4370021872 | 0.063752375 |
| 23 | 10 | 10 | 0.479882317 | 0.062964 |
| 24 | 10 | 12 | 0.5199262805 | 0.062786125 |
| 25 | 10 | 16 | 0.5935128644 | 0.062229125 |
| 26 | 10 | 24 | 0.6796541184 | 0.061876875 |
| 27 | 10 | 32 | 0.7379264548 | 0.0618305 |
| 28 | 10 | 48 | 0.8053073436 | 0.061516125 |
| 29 | 10 | 64 | 0.8465657607 | 0.061403 |
| 30 | 20 | 1 | 0.06401134632 | 0.757281 |
| 31 | 20 | 2 | 0.3653038088 | 0.06925225 |
| 32 | 20 | 3 | 0.5262307867 | 0.06323275 |
| 33 | 20 | 4 | 0.6446151808 | 0.06203725 |
| 34 | 20 | 5 | 0.7232914954 | 0.06185625 |
| 35 | 20 | 6 | 0.7802997425 | 0.06165275 |
| 36 | 20 | 8 | 0.8357323289 | 0.061493375 |
| 37 | 20 | 10 | 0.8705290645 | 0.061302375 |
| 38 | 20 | 12 | 0.8942843586 | 0.061368125 |
| 39 | 20 | 16 | 0.9240833685 | 0.06133 |
| 40 | 20 | 24 | 0.9489109725 | 0.061323375 |
| 41 | 20 | 32 | 0.9614683628 | 0.06120425 |
| 42 | 20 | 48 | 0.97474062 | 0.061322875 |
| 43 | 20 | 64 | 0.9812480465 | 0.06146825 |