Download |WORK| Ssdeep
Eufemia Graybill
The configure script can accept lots of options. Run ./configure --help for the complete list.The most common option used is the --prefix option which installs the program in a locationother than the default, /usr/local.If you wanted to install the program elsewhere, for example, /tmp/ssdeep,you would run ./configure --prefix=/tmp/ssdeep instead.
Notice how the above output shows the full path in the filename.You can have ssdeep print relative filenames instead of absolute ones.That is, omit all of the path information except that specified on the command line.To enable relative paths, use the -l flag. Repeating our first example with the -l flag:
Normally, attempting to process a directory will generate an error message.Under recursive mode, ssdeep will hash specified files and files in specified directory including its subdirectories.Recursive mode is activated by using the -r flag.
One of the more powerful features of ssdeep is the ability to match the hashes of input files against a list of known hashes.Because of inexact nature of fuzzy hashing, note that just because ssdeep indicates that two files match,it does not mean that those files are related.You should examine every pair of matching files individually to see how well they correspond.
As a more practical example of ssdeep's matching functionality, you can usessdeep's matching mode to help find source code reuse.Let's say we have two folders, ssdeep-1.1 and md5deep-1.12that contain the source code for each of those tools.You can compare their contents by computing fuzzy hashes for one tree and then comparing them against the other:
You can also compare many without writing out any hashes to the disk using two different methods.Let's say that we have a whole bunch of files in two or three directories and want to know which ones are similar to each other.We can use the -d mode to display these matches.The switch causes ssdeep to compute a fuzzy hash for each input file and compare it against all of the other input files.
ssdeep is a program for computing context triggered piecewise hashes (CTPH).Also called fuzzy hashes, CTPH can match inputs that have homologies.Such inputs have sequences of identical bytes in the same order, although bytes in between these sequences may be different in both content and length.
If the distribution you use does not provide ssdeep package, you will need to build it yourself.Download the source code from GitHub project page and install it.It should work at most of GNU Autotools-compatible environment.
ssdeep is available at GitHub.The latest version is 2.14.1 (released on 2017-11-07). You can take a look at the complete changelog, but here are the changes in the latest version:
- Optimizations to the fuzzy hashing engine (hash generator can run as twice as fast and comparison can run 1.5 through 5 times faster [heavily depends on the data and platform] than the previous release)
- Fixed issue when certain memory allocation is failed
He mainly contributed to ssdeep version 2.10 and 2.11.Thanks to his re-written fuzzy hashing engine, libfuzzy can now be used from multi-threaded programsand is capable to process streams without seek capabilities.
This is a straightforward Python wrapper for ssdeep by Jesse Kornblum, which is a library for computing contexttriggered piecewise hashes (CTPH). Also called fuzzy hashes, CTPH can match inputs that have homologies. Such inputshave sequences of identical bytes in the same order, although bytes in between these sequences may be different in bothcontent and length.
I found a solution. Essentially what's going on is that Homebrew installs ssdeep in a location that the ssdeep PyPi package is not expecting. You can point the PyPi package to the correct locations with the following steps.
ssdeep package at PyPI is a Python wrapper for ssdeep library written in C. So first you have to compile and install ssdeep, then other python-ssdeep requirements, then compile and install python-ssdeep.
A Rust wrapper for ssdeep by JesseKornblum, which is a C libraryfor computing context triggered piecewisehashes (CTPH). Alsocalled fuzzy hashes, CTPH can match inputs that have homologies. Suchinputs have sequences of identical bytes in the same order, although bytesin between these sequences may be different in both content and length. Incontrast to standard hashing algorithms, CTPH can be used to identify filesthat are highly similar but not identical.
AV vendors will have a list of existing well-known malware and its ssdeep hash. Here I have calculated the SSDEEP hash for the original file njRAT v0.7d.exe ( Before obfuscation ). Context triggered piecewise hashing ( CTPH ) is up and ready now.
When we compare the obfuscated file with existing malware ssdeep file hashes , It is found that 99% of the content is matched with an existing malware sample. Hope now we can mark this unknown file as malicious.
SSDEEP is a fuzzy hashing tool written by Jesse Kornblum. There is quite a bit of work about similarity hashing and comparisons with other methods. The mainstream tools for digital forensics, however, appear to be ssdeep and sdhash. For example, NIST created hash sets using both tools. I wrote a post about sdhash in 2012 if you want to know a little more about how it works.
So far we can see that ssdeep hashes are much larger that MD5 hashes. That means storing a large number of fuzzy hashes will take a lot more space, so we need to consider when fuzzy hashing is most useful for our investigations.
FuzzyBytes computes the fuzzy hash of a slice of byte.It is the caller's responsibility to append the filename, if any, to result after computation.Returns an error when ssdeep could not be computed on the buffer.
FuzzyFile computes the fuzzy hash of a file using os.File pointer.FuzzyFile will computes the fuzzy hash of the contents of the open file, starting at the beginning of the file.When finished, the file pointer is returned to its original position.If an error occurs, the file pointer's value is undefined.It is the callers's responsibility to append the filename to the result after computation.Returns an error when ssdeep could not be computed on the file.
FuzzyFilename computes the fuzzy hash of a file.FuzzyFilename will opens, reads, and hashes the contents of the file 'filename'.It is the caller's responsibility to append the filename to the result after computation.Returns an error when the file doesn't exist or ssdeep could not be computed on the file.
FuzzyReader computes the fuzzy hash of a Reader interface with a given input size.It is the caller's responsibility to append the filename, if any, to result after computation.Returns an error when ssdeep could not be computed on the Reader.
The search service can find package by either name (apache),provides(webserver), absolute file names (/usr/bin/apache),binaries (gprof) or shared libraries (libXm.so.2) instandard path. It does not support multiple arguments yet... The System and Arch are optional added filters, for exampleSystem could be "redhat", "redhat-7.2", "mandrake" or "gnome", Arch could be "i386" or "src", etc. depending on your system. System Arch RPM resource ssdeepComputes a checksum based on context triggered piecewise hashes foreach input file. If requested, the program matches those checksumsagainst a file of known checksums and reports any possible matches.Output is written to standard out and errors to standard error.Input from standard input is not supported.
I'm interested in developing plugins for fuzzy hashes, including ssdeep, sdhash, and LZJD. I did find something similar, an existing ssdeep plugin for Elasticsearch in Python, but I'd prefer to write the code in Java, since it's likely faster, and some of the fuzzy hash code is in Java anyway.
I'm working on a project where I'd like to be able to find similar documents based on the fuzzy hash, and the documents are metadata for raw binary files. These fuzzy hashes would work for text documents too (ssdeep was developed to detect spam emails).
CTPH uses trigger points instead of fixed-sized blocks. Everytime a specific trigger point hits, the algorithms calculates a hash value of the current chunk of data. The conditions for the trigger points are chosen in such a way that the final hash value doesn't grow arbitrarily in size with increased input data size. E.g., ssdeep has a desired number of 64 chunks per input file, so the trigger point is dependent on the size of input data. To compare two files, ssdeep uses an edit distance algorithm: The more steps it takes to transform one ssdeep hash value to the other, the less similar the files are.
The development of ssdeep was a milestone at the time. New hashing algorithms which improve certain aspects of ssdeep have been created since. E.g., SimFD has a better false positive rate and MRSH improved security aspects of ssdeep [breitinger13]. The author's website states that ssdeep is still often preferred due to its speed (e.g., compared to TLSH) and it is the "de facto standard" for fuzzy hashing algorithms used for malware samples and their classification. Sample databases like VirusTotal and Malwarebazaar support it.
TLSH stands for Trend-Micro Locality Sensitive Hash, which was published in a paper in 2013 [oliver13]. According to their paper TLSH has better accuracy than ssdeep when classifying malware samples [p.12, oliver13]. Just like ssdeep it is a CTPH. TLSH is supported by VirusTotal.
The idea of SIF hashing is to find features of a file that are unlikely present by chance and compare those features to other files. Sdhash uses entropy calculation to pick the relevant features and then creates the hash value based on them. That also means sdhash cannot fully cover a file and modifications to a file may not influence the hash value at all if they are not part of a statistically-improbable feature. Sdhash shows better accuracy than ssdeep when classifying malware samples [p.12, oliver13][roussev11]. However, its strong suit is the detection of fragments and not comparison of files [p.8, breitinger12].
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