Git Product home page Git Product logo

ssdeepr's Introduction

Project Status: Active – The project has reached a stable, usable state and is being actively developed. Signed by Signed commit % Linux build Status
Minimal R Version License

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.

ssdeepr's People

Contributors

hrbrmstr avatar

Stargazers

avatar avatar avatar avatar

Watchers

avatar avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.