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https://codeberg.org/scip/valpass.git
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added arithmetic mean
This commit is contained in:
25
README.md
25
README.md
@@ -21,7 +21,7 @@ with go as a reusable module.
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## Features
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- standalone module without external dependencies
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- uses 3 different metrics to measure password quality
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- uses 5 different metrics to measure password quality
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- you can configure which metric to use
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- you can also configure the quality thresholds
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- there's support for dictionary lookup, but you need to provide the dictionary yourself
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@@ -88,6 +88,21 @@ Of course we do not use RLE. We measure compression
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using the [Flate algorithm](
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https://en.m.wikipedia.org/wiki/Deflate).
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### Optional: arithmetic mean value
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This is simply the result of summing the all the printable ascii chars
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divided by password length. The ideal value would be ~80, because most
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normal letters hang out in the upper area between 32 (space) and
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126(tilde). We consider a password ok, if its mean lies around this
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area give or take 5. If the mean departs more from this value, the
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characters are consistently high or low (e.g. more numbers and upper
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case letters or only lower case letters). The latter, 5, can be
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tweaked. The larger the number, tha laxer the result.
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Please be warned, that this metric will in most cases give you bad
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results on otherwise good passwords, such as diceware passwords. Only
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use it if you know what you're doing.
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### Optional: dictionary check
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You can supply a dictionary of words of your
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@@ -103,12 +118,8 @@ you can tune the quality thresholds as needed.
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### Future/ ToDo
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- checksum test using supplied checksum list, e.g. of leaked passwords
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- fuzzy testing against dictionary to catch variations
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- chi square test (see http://www.fourmilab.ch/random/)
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- Arithmetic mean value test
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- Monte Carlo value test
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- Serial correlation
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- maybe some dieharder tests
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- fuzzy testing against dictionary to catch variations, using
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Levenshtein or something similar.
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## Usage
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@@ -16,7 +16,8 @@ Compression rate 0%% min %d%% %d%%
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Character distribution 100%% min %0.2f%% %0.2f%%
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Character entropy 8.0 bits/char min %0.2f %0.2f bits/char
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Character redundancy 0.0%% max %0.2f%% %0.2f%%
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Dictionary match false false %t
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Dictionary match false false %t
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Arithmetic mean 80 true %0.2f
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------------------------------------------------------------------
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Validation response %t
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`
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@@ -28,6 +29,7 @@ func main() {
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Entropy: valpass.MIN_ENTROPY,
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Dictionary: &valpass.Dictionary{Words: ReadDict("t/american-english")},
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UTF8: false,
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Mean: 20, //valpass.LIMIT_MEAN,
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}
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res, err := valpass.Validate(os.Args[1], opts)
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@@ -46,6 +48,7 @@ func main() {
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100-opts.CharDistribution,
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100-res.CharDistribution,
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res.DictionaryMatch,
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res.Mean,
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res.Ok,
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)
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47
lib.go
47
lib.go
@@ -26,6 +26,7 @@ type Options struct {
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Entropy float64 // minimum entropy value in bits/char
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Dictionary *Dictionary // if set, lookup given dictionary, the caller provides it
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UTF8 bool // if true work on unicode utf-8 space, not just bytes
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Mean float64 // if >0, calculate the arithmetic mean
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}
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/*
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@@ -43,6 +44,14 @@ const (
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// we start our ascii arrays at char(32), so to have max 95
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// elements in the slice, we subtract 32 from each ascii code
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MIN_ASCII byte = 32
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// arithmetic mean limits: we work on chr(32) til chr(126) in
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// ascii. The mean value, however, is not 63 as one would suppose,
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// but 80, because most used printable ascii chars exist in the
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// upper area of the space. So, we take 80 as the middle ground
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// and go beyond 5 up or down
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MIDDLE_MEAN float64 = 80
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LIMIT_MEAN float64 = 5
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)
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/*
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@@ -54,6 +63,7 @@ type Result struct {
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Compress int // actual compression rate in percent
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CharDistribution float64 // actual character distribution in percent
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Entropy float64 // actual entropy value in bits/chars
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Mean float64 // actual arithmetic mean, close to 127.5 is best
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}
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/*
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@@ -70,7 +80,8 @@ func Validate(passphrase string, opts ...Options) (Result, error) {
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MIN_DIST,
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MIN_ENTROPY,
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nil,
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false,
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false, // dict: default off
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0, // mean: default off
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}
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if len(opts) == 1 {
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@@ -144,6 +155,16 @@ func Validate(passphrase string, opts ...Options) (Result, error) {
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}
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}
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if options.Mean > 0 {
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mean := GetArithmeticMean(passphrase)
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if mean > (MIDDLE_MEAN+options.Mean) || mean < (MIDDLE_MEAN-options.Mean) {
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result.Ok = false
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}
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result.Mean = mean
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}
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return result, nil
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}
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@@ -316,3 +337,27 @@ func GetDictMatch(passphrase string, dict *Dictionary) (bool, error) {
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return false, nil
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}
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/*
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* Return the arithmetic mean value:
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This is simply the result of summing the all the bytes (bits if the
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-b option is specified) in the file and dividing by the file
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length. If the data are close to random, this should be about 127.5
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(0.5 for -b option output). If the mean departs from this value, the
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values are consistently high or low.
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Working on US-ASCII space
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*/
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func GetArithmeticMean(passphrase string) float64 {
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sum := 0.0
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count := 0.0
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for _, char := range []byte(passphrase) {
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sum += float64(char)
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count++
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}
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return sum / count
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}
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17
lib_test.go
17
lib_test.go
@@ -182,6 +182,10 @@ var opts_dict = valpass.Options{
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UTF8: false,
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}
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var opts_mean = valpass.Options{
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Mean: 15, // very lax in order to succeed!
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}
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var goodtests = []Tests{
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{
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name: "checkgood",
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@@ -195,6 +199,14 @@ var goodtests = []Tests{
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},
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}
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var meantests = []Tests{
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{
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name: "checkgood-mean",
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want: true,
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opts: opts_mean,
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},
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}
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var badtests = []Tests{
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{
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name: "checkbad",
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@@ -231,6 +243,11 @@ func TestValidate(t *testing.T) {
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}
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}
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for _, tt := range meantests {
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for _, pass := range pass_random_good {
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CheckPassword(t, pass, tt.name, tt.want, tt.opts)
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}
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}
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}
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func CheckPassword(t *testing.T, password string,
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