impfuzzy VS pyDenStream

Compare impfuzzy vs pyDenStream and see what are their differences.

impfuzzy

Fuzzy Hash calculated from import API of PE files (by JPCERTCC)

pyDenStream

Implementation of the DenStream algorithm in Python. (by MrParosk)
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impfuzzy pyDenStream
1 1
65 6
- -
0.3 0.0
about 1 year ago about 1 month ago
Python Python
- -
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

impfuzzy

Posts with mentions or reviews of impfuzzy. We have used some of these posts to build our list of alternatives and similar projects.
  • Where do you get old versions of Visual C++?
    1 project | reddit.com/r/learnpython | 25 Apr 2021
    I want to use this plugin so I try to install its required module, which fails (whether using 'pip' or 'setup.py') for the same reason as distorm3. The installation of ssdeep as required by pyimpfuzzy also fails, but for a different reason that I haven't started looking into yet.

pyDenStream

Posts with mentions or reviews of pyDenStream. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing impfuzzy and pyDenStream you can also consider the following projects:

uis-rnn - This is the library for the Unbounded Interleaved-State Recurrent Neural Network (UIS-RNN) algorithm, corresponding to the paper Fully Supervised Speaker Diarization.

aurora - Malware similarity platform with modularity in mind.

Python-Rootkit - Python Remote Administration Tool (RAT) to gain meterpreter session

stringlifier - Stringlifier is on Opensource ML Library for detecting random strings in raw text. It can be used in sanitising logs, detecting accidentally exposed credentials and as a pre-processing step in unsupervised ML-based analysis of application text data.