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Þ'¯°ÌME[YéïĵÂCå½ Ué©Áû'Ê9%eÔðNU”ë‘ÌsD3/®+UI˜9h.WC”빓$#:pz:YÓ ¿xž* ³$Í +$kñAŠ‹†¢ Uê>¸)_š¬÷©ßAÂÔb9ÇU ¯¾á•9¯ÏÏ÷O÷¼¼Fähal1‰3Ì[Ïr•´UCksNÐ] R‘¸¥H+§Šé†c©vÖÞ0iÓ76s†î!§=ß ¼~Ô'°Ãmäoäš³ªøi1úÉ)³yV8 CLÄØÁ‘WYïi€H6ÖÑiámø^ÈY´°Ñ7¥Û*—Ñ©L«Qƒï—Ùrÿ ›£Ð*š¸ˆL©ˆ$ˆ ÷¾D§9È®«qbqC)–ˆïv´çñsÑVT­Ø, <àïºÀO«Jý·õ àfPìð .wFšir´þ’2_Y *Æ€x\« ì€9š@ Ž|F⇥ˆkZ@hÖÄ0t¿-<“‹qµ¾*ZL¤Ú)&BJpÓF5=$„at*Zš$’ÑtdûÝRI1 2Ž‰$€$I$#‰SÞ’Hë¬ï;Á$¡t$’`<(ñÇt)$‡Ð.Êf¢X’Kt=Éé$‚ˆªè¢oÝëòI%Rgcª÷ŠyI%¡‰ÿ !ñ)´õ $¤ Ô’IIGÿÙ--- -- Formula functions for various calculations. -- -- The library lets scripts to use common mathematical functions to compute percentages, -- averages, entropy, randomness and other calculations. Scripts that generate statistics -- and metrics can also make use of this library. -- -- @copyright Same as Nmap--See https://nmap.org/book/man-legal.html --- local math = require "math" local stdnse = require "stdnse" local string = require "string" local table = require "table" local unittest = require "unittest" _ENV = stdnse.module("formulas", stdnse.seeall) --- Calculate the entropy of a password. -- -- A random password's information entropy, H, is given by the formula: H = L * -- (logN) / (log2), where N is the number of possible symbols and L is the -- number of symbols in the password. Based on -- https://en.wikipedia.org/wiki/Password_strength -- @param value The password to check -- @return The entropy in bits calcPwdEntropy = function(value) local total, hasdigit, haslower, hasupper, hasspaces = 0, 0, 0, 0, false if string.find(value, "%d") then hasdigit = 1 end if string.find(value, "%l") then haslower = 1 end if string.find(value, "%u") then hasupper = 1 end if string.find(value, ' ') then hasspaces = true end -- The values 10, 26, 26 have been taken from Wikipedia's entropy table. local total = hasdigit * 10 + hasupper * 26 + haslower * 26 local entropy = math.floor(math.log(total) * #value / math.log(2)) return entropy end -- A chi-square test for the null hypothesis that the members of data are drawn -- from a uniform distribution over num_cats categories. local function chi2(data, num_cats) local bins = {} local x2, delta, expected for _, x in ipairs(data) do bins[x] = bins[x] or 0 bins[x] = bins[x] + 1 end expected = #data / num_cats x2 = 0.0 for _, n in pairs(bins) do delta = n - expected x2 = x2 + delta * delta end x2 = x2 / expected return x2 end local function c_to_bin (c) local n = stdnse.tobinary(c:byte()) return ("0"):rep(8-#n)..n end -- Split a string into a sequence of bit strings of the given length. -- splitbits("abc", 5) --> {"01100", "00101", "10001", "00110"} -- Any short final group is omitted. local function splitbits(s, n) local bits = s:gsub(".", c_to_bin) local seq = {} for i = 1, #bits - n, n do seq[#seq + 1] = bits:sub(i, i + n - 1) end return seq end -- chi-square cdf table at 0.95 confidence for different degrees of freedom. -- >>> import scipy.stats, scipy.optimize -- >>> scipy.optimize.newton(lambda x: scipy.stats.chi2(dof).cdf(x) - 0.95, dof) local CHI2_CDF = { [3] = 7.8147279032511738, [15] = 24.99579013972863, [255] = 293.2478350807001, } --- Checks whether a sample looks random -- -- Because our sample is so small (only 16 bytes), do a chi-square -- goodness of fit test across groups of 2, 4, and 8 bits. If using only -- 8 bits, for example, any sample whose bytes are all different would -- pass the test. Using 2 bits will tend to catch things like pure -- ASCII, where one out of every four samples never has its high bit -- set. -- @param data The data to check -- @return True if the data appears to be random, false otherwise function looksRandom(data) local x2 x2 = chi2(splitbits(data, 2), 4) if x2 > CHI2_CDF[3] then return false end x2 = chi2(splitbits(data, 4), 16) if x2 > CHI2_CDF[15] then return false end x2 = chi2({string.byte(data, 1, -1)}, 256) if x2 > CHI2_CDF[255] then return false end return true end --- Return the mean and sample standard deviation of an array, using the -- algorithm from Knuth Vol. 2, Section 4.2.2. -- -- @params t An array-style table of values -- @return The mean of the values -- @return The standard deviation of the values function mean_stddev(t) local i, m, s, sigma if #t == 0 then return nil, nil elseif #t == 1 then return t[1], 0 end m = t[1] s = 0 for i = 2, #t do local mp = m m = m + (t[i] - m) / i s = s + (t[i] - mp) * (t[i] - m) end sigma = math.sqrt(s / (#t - 1)) return m, sigma end -- Partition function for quickselect and quicksort local function partition(t, left, right, pivot) local pv = t[pivot] t[pivot], t[right] = t[right], t[pivot] local storeidx = left for i=left, right-1 do assert(storeidx < right) if t[i] < pv then t[storeidx], t[i] = t[i], t[storeidx] storeidx = storeidx + 1 end end t[storeidx], t[right] = t[right], t[storeidx] return storeidx end -- Quickselect algorithm local function _qselect(t, left, right, k) if left == right then return t[left] end local pivot = math.random(left, right) pivot = partition(t, left, right, pivot) if k == pivot then return t[k] elseif k < pivot then return _qselect(t, left, pivot - 1, k) else return _qselect(t, pivot + 1, right, k) end end --- Return the k-th largest element in a list -- -- @param t The list, not sorted -- @param k The ordinal value to return -- @return The k-th largest element in the list function quickselect(t, k) local tc = {} -- Work on a copy of the table, since we modify in-place table.move(t, 1, #t, 1, tc) return _qselect(tc, 1, #tc, k) end --- Find the median of a list -- --@param t the table/list of values --@return the median value function median(t) return quickselect(t, math.ceil(#t/2)) end if not unittest.testing() then return _ENV end test_suite = unittest.TestSuite:new() local table_equal = unittest.table_equal test_suite:add_test(table_equal(splitbits("abc", 5), {"01100", "00101", "10001", "00110"}), 'splitbits("abc", 5)') return _ENV