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drwxr-xr-x
2024-08-29 14:21
afp.lua
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ajp.lua
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amqp.lua
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anyconnect.lua
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asn1.lua
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base32.lua
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base64.lua
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bin.lua
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bitcoin.lua
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bits.lua
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bittorrent.lua
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bjnp.lua
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brute.lua
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cassandra.lua
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citrixxml.lua
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coap.lua
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comm.lua
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creds.lua
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cvs.lua
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datafiles.lua
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datetime.lua
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dhcp.lua
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dhcp6.lua
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dns.lua
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drda.lua
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eap.lua
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formulas.lua
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ftp.lua
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geoip.lua
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giop.lua
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http.lua
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httpspider.lua
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iax2.lua
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imap.lua
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informix.lua
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ipOps.lua
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ipmi.lua
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ipp.lua
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irc.lua
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iscsi.lua
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jdwp.lua
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json.lua
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knx.lua
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ldap.lua
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lfs.luadoc
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libssh2-utility.lua
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libssh2.luadoc
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listop.lua
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lpeg-utility.lua
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lpeg.luadoc
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ls.lua
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match.lua
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membase.lua
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mobileme.lua
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mongodb.lua
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mqtt.lua
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msrpc.lua
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msrpcperformance.lua
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msrpctypes.lua
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mssql.lua
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multicast.lua
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mysql.lua
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natpmp.lua
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nbd.lua
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ncp.lua
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ndmp.lua
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netbios.lua
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nmap.luadoc
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nrpc.lua
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nsedebug.lua
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omp2.lua
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oops.lua
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openssl.luadoc
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ospf.lua
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packet.lua
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pcre.luadoc
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pgsql.lua
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pop3.lua
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pppoe.lua
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proxy.lua
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punycode.lua
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rand.lua
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rdp.lua
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re.lua
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redis.lua
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rmi.lua
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rpc.lua
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rpcap.lua
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rsync.lua
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rtsp.lua
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sasl.lua
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shortport.lua
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sip.lua
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slaxml.lua
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smb.lua
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smb2.lua
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smbauth.lua
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smtp.lua
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snmp.lua
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socks.lua
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srvloc.lua
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ssh1.lua
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ssh2.lua
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sslcert.lua
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sslv2.lua
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stdnse.lua
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strbuf.lua
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strict.lua
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stringaux.lua
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stun.lua
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tab.lua
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tableaux.lua
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target.lua
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tftp.lua
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tls.lua
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tn3270.lua
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tns.lua
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unicode.lua
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unittest.lua
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unpwdb.lua
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upnp.lua
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url.lua
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versant.lua
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vnc.lua
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vulns.lua
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wsdd.lua
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xdmcp.lua
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xmpp.lua
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zlib.luadoc
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Save
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--- -- 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 -- 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(".", function (c) local n = stdnse.tobinary(c:byte()) return ("0"):rep(8-#n)..n end) 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