~hrbrmstr/ssdeepr

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a8506c7c — boB Rudis 2 months ago
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ssdeepr

Context Triggered Piecewise Hash Computation Using ‘ssdeep’

Description

The ssdeep project provides an open source library https://github.com/ssdeep-project/ssdeep/ context triggered piecewise hashing. Methods are provided to compute and compare hashes from character/byte streams.

What’s Inside The Tin

The following functions are implemented:

  • check_is_xptr: Test whether an object is an external pointer
  • hash_compare: Compare two hashes
  • hash_con: Return CTP hash of one data collected from a connection
  • hash_file: Return CTP hash of one or more files
  • hash_raw: Return CTP hash of a raw vector
  • is_null_xptr_: Test whether an external pointer is null
  • split_hash: Splits an ssdeep hash string into component parts

Installation

You’ll need libfuzzy installed and available for linking. See https://ssdeep-project.github.io/ssdeep/index.html#platforms for platform support.

On Ubuntu/Debian you can do:

sudo apt install libfuzzy-dev

On macOS you can do:

brew install ssdeep

The library works on Windows, I just need to do some manual labor for that.

Package installation:

install.packages("ssdeepr", repos = c("https://cinc.rud.is", "https://cloud.r-project.org/"))
# or
remotes::install_git("https://git.rud.is/hrbrmstr/ssdeepr.git")
# or
remotes::install_git("https://git.sr.ht/~hrbrmstr/ssdeepr")
# or
remotes::install_gitlab("hrbrmstr/ssdeepr")
# or
remotes::install_bitbucket("hrbrmstr/ssdeepr")
# or
remotes::install_github("hrbrmstr/ssdeepr")

NOTE: To use the ‘remotes’ install options you will need to have the {remotes} package installed.

Usage

library(ssdeepr)

# current version
packageVersion("ssdeepr")
## [1] '0.2.0'
  • index.html is a static copy of a blog main page with a bunch of <div>s with article snippets
  • index1.html is the same file as index.htmnl with a changed cache timestamp at the end
  • index2.html is the same file as index.html with one article snippet removed
  • RMacOSX-FAQ.html is the CRAN ‘R for Mac OS X FAQ’
system.file("extdat", package="ssdeepr") %>% 
  list.files(full.names = TRUE, pattern = "html$", include.dirs = FALSE) %>% 
  hash_file() -> hashes

hashes
##                                                                                             path
## 1       /Library/Frameworks/R.framework/Versions/3.6/Resources/library/ssdeepr/extdat/index.html
## 2      /Library/Frameworks/R.framework/Versions/3.6/Resources/library/ssdeepr/extdat/index1.html
## 3      /Library/Frameworks/R.framework/Versions/3.6/Resources/library/ssdeepr/extdat/index2.html
## 4 /Library/Frameworks/R.framework/Versions/3.6/Resources/library/ssdeepr/extdat/RMacOSX-FAQ.html
##                                                                                                     hash
## 1 1536:rwjgwyHuuoH3yHgHJBH5H3YHwHuXiOXd6uEk9SWLIL7ERKvc4wHc+sius234Y4NY:rZvb7HHc+sius234Y4N4pqwrCihwnUui
## 2 1536:twjgwyHuuoH3yHgHJBH5H3YHwHuXiCe6uEk9SWLIL7ERKvc4wbc+sius234Y4N4j:tZvbPobc+sius234Y4N4pqwrCihwnUua
## 3 1536:twjgwyHuuoH3yHgHJBH5H3YHwHuXiCJEk9SWLIL7ERKvc4wbc+sius234Y4N4pqs:tZvbPHbc+sius234Y4N4pqwrCihwnUum
## 4                                1536:3ExSauOOiqyq5tfAJqE3+OmEvqVtEYsSWiWB/H5ZJ:0x9fqyqtfAJqEu8vOWYsLd5r

hash_compare(hashes$hash[1], hashes$hash[1])
## [1] 100
hash_compare(hashes$hash[1], hashes$hash[2])
## [1] 91
hash_compare(hashes$hash[1], hashes$hash[3])
## [1] 88
hash_compare(hashes$hash[1], hashes$hash[4])
## [1] 0

Works with Connections, too. All three should be the same if the Wikipedia page hasn’t changed since making local copies in the package.

Using hash_con() has the advantage of not requiring the entire contents of the target blob to be read into memory in exchange for a tiny cost to speed for most files (see Benchmarks).

NOTE that retrieving the URL contents with different user-agent strings and/or with javascript-enabled may/will likely generate different content and, thus, a different hash.

(k1 <- hash_con(url("https://en.wikipedia.org/wiki/Donald_Knuth", 
                    header = setNames(splashr::ua_ios_safari, "User-Agent"))))
## [1] "1536:IWaFR+jsCHr6UVyn1KSLnGURhAa0qYHaYF8tUdkWO9F+mTi9f0ruvSWnqd4:GWL6EOKsGMYJF8t99EBxznw4"

(k2 <- hash_con(file(system.file("knuth", "local.html", package = "ssdeepr"))))
## [1] "3072:u2dfqECHC6NPsWzqFg2qDKgNYsVeJb19pEDTlfrd5czRsZNqqelzPFKsuXs6X9pU:PQli6NPsWzcg2/EYsVUY6sI"

(k3 <- hash_con(gzfile(system.file("knuth", "local.gz", package = "ssdeepr"))))
## [1] "3072:u2dfqECHC6NPsWzqFg2qDKgNYsVeJb19pEDTlfrd5czRsZNqqelzPFKsuXs6X9pU:PQli6NPsWzcg2/EYsVUY6sI"

hash_compare(k1, k2)
## [1] 0

hash_compare(k1, k3)
## [1] 0

hash_compare(k2, k3)
## [1] 100

Benchmarks

microbenchmark::microbenchmark(
  con = hash_con(file(system.file("knuth", "local.html", package = "ssdeepr"))),
  gzc = hash_con(gzfile(system.file("knuth", "local.gz", package = "ssdeepr"))),
  fil = hash_file(system.file("knuth", "local.html", package = "ssdeepr"))
)
## Unit: milliseconds
##  expr      min       lq     mean   median       uq       max neval
##   con 5.902252 6.004019 6.410288 6.130342 6.531428 16.202922   100
##   gzc 6.717070 6.821839 7.175860 7.037044 7.540798  8.369695   100
##   fil 5.129446 5.218194 5.559326 5.392933 5.692457  7.002297   100

ssdeepr Metrics

Lang # Files (%) LoC (%) Blank lines (%) # Lines (%)
C++ 2 0.13 168 0.49 45 0.34 26 0.14
R 10 0.67 92 0.27 39 0.30 96 0.51
Bourne Shell 2 0.13 53 0.16 10 0.08 14 0.07
Rmd 1 0.07 28 0.08 37 0.28 53 0.28

Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.