r/ghidra
subreddit for the NSA reverse engineering framework Ghidra
en
Community dedicated to discussion about the National Security Agency's reverse engineering framework, Ghidra.
Community dedicated to discussion about the National Security Agency's reverse engineering framework, Ghidra.
Share posts about your workflow, tips and tricks, neat things you've done lately, or ask questions about how best to use GHIDRA.
Stay relevant to the subreddit's topic, don't attack other ...
4,438
January 5, 2019
public
all_ads
Total Submissions
747
Total Comments
2,337
Earliest Submission
January 5, 2019
Earliest Comment
March 5, 2019
Rank↕ | Word↕ | Occurrences in Subreddit | Total Occurrences | Rate in Subreddit (per 1M words) | Rate in Reddit (per 1M words) | Ratio (Sub Rate / Reddit Rate) |
|---|---|---|---|---|---|---|
| #1 | 192 | 21,131 | 2871.8 | 0.2 | 16298.57 | |
| #2 | 47 | 31,266 | 703.0 | 0.3 | 2696.46 | |
| #3 | 35 | 45,949 | 523.5 | 0.4 | 1366.34 | |
| #4 | 36 | 63,093 | 538.5 | 0.5 | 1023.50 | |
| #5 | 80 | 141,089 | 1196.6 | 1.2 | 1017.10 | |
| #6 | 50 | 92,150 | 747.9 | 0.8 | 973.29 | |
| #7 | 34 | 69,316 | 508.6 | 0.6 | 879.86 | |
| #8 | 31 | 65,561 | 463.7 | 0.5 | 848.17 | |
| #9 | 42 | 92,643 | 628.2 | 0.8 | 813.21 | |
| #10 | 31 | 74,896 | 463.7 | 0.6 | 742.46 | |
| #11 | 34 | 95,277 | 508.6 | 0.8 | 640.12 | |
| #12 | 33 | 140,836 | 493.6 | 1.2 | 420.31 | |
| #13 | 54 | 256,593 | 807.7 | 2.1 | 377.50 | |
| #14 | 71 | 373,452 | 1062.0 | 3.1 | 341.03 | |
| #15 | 30 | 160,643 | 448.7 | 1.3 | 334.99 | |
| #16 | 39 | 223,727 | 583.3 | 1.9 | 312.69 | |
| #17 | 36 | 252,656 | 538.5 | 2.1 | 255.59 | |
| #18 | 47 | 345,335 | 703.0 | 2.9 | 244.13 | |
| #19 | 31 | 249,986 | 463.7 | 2.1 | 222.44 | |
| #20 | 91 | 785,260 | 1361.1 | 6.5 | 207.87 | |
| #21 | 59 | 541,877 | 882.5 | 4.5 | 195.31 | |
| #22 | 51 | 477,369 | 762.8 | 4.0 | 191.64 | |
| #23 | 41 | 399,752 | 613.3 | 3.3 | 183.98 | |
| #24 | 111 | 1,240,404 | 1660.3 | 10.3 | 160.52 | |
| #25 | 37 | 421,785 | 553.4 | 3.5 | 157.35 | |
| #26 | 130 | 1,571,531 | 1944.5 | 13.1 | 148.38 | |
| #27 | 112 | 1,434,456 | 1675.2 | 12.0 | 140.06 | |
| #28 | 56 | 770,947 | 837.6 | 6.4 | 130.30 | |
| #29 | 42 | 637,307 | 628.2 | 5.3 | 118.21 | |
| #30 | 40 | 639,367 | 598.3 | 5.3 | 112.22 | |
| #31 | 43 | 695,865 | 643.2 | 5.8 | 110.84 | |
| #32 | 52 | 862,920 | 777.8 | 7.2 | 108.09 | |
| #33 | 34 | 606,538 | 508.6 | 5.1 | 100.55 | |
| #34 | 273 | 5,123,036 | 4083.4 | 42.7 | 95.59 | |
| #35 | 37 | 731,112 | 553.4 | 6.1 | 90.78 | |
| #36 | 34 | 820,891 | 508.6 | 6.8 | 74.30 | |
| #37 | 45 | 1,193,988 | 673.1 | 10.0 | 67.61 | |
| #38 | 68 | 2,013,194 | 1017.1 | 16.8 | 60.59 | |
| #39 | 37 | 1,102,709 | 553.4 | 9.2 | 60.19 | |
| #40 | 256 | 7,750,568 | 3829.1 | 64.6 | 59.25 | |
| #41 | 49 | 1,500,287 | 732.9 | 12.5 | 58.59 | |
| #42 | 223 | 7,000,268 | 3335.5 | 58.4 | 57.14 | |
| #43 | 31 | 1,010,870 | 463.7 | 8.4 | 55.01 | |
| #44 | 90 | 2,945,417 | 1346.2 | 24.6 | 54.81 | |
| #45 | 108 | 3,573,754 | 1615.4 | 29.8 | 54.21 | |
| #46 | 77 | 2,561,969 | 1151.7 | 21.4 | 53.91 | |
| #47 | 68 | 2,688,553 | 1017.1 | 22.4 | 45.37 | |
| #48 | 38 | 1,503,990 | 568.4 | 12.5 | 45.32 | |
| #49 | 43 | 1,864,498 | 643.2 | 15.5 | 41.37 | |
| #50 | 77 | 3,389,200 | 1151.7 | 28.3 | 40.75 |