A Machine Learning approach for classifying a file as Malicious or Legitimate
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Updated
Oct 10, 2016 - Jupyter Notebook
A Machine Learning approach for classifying a file as Malicious or Legitimate
Pypy.js compatible version of pefile.py for use in offline browser implementation
PE Bliss - Cross-Platform Portable Executable C++ Library
PE Tools - Portable executable (PE) manipulation toolkit
Hex Workshop editor's structure library for the Microsoft's Portable Executable format.
Malware Data Science Reading Diary / Notes
packer identification tool using SVM
View and edit Portable Exexutable (PE) files.
PE Binary Shellcode Injector - Automated code cave discovery, shellcode injection, ASLR bypass, x86/x64 compatible
Program in JScript.NET to detect if a PE Image is compiled for 32 or 64 bit CPU.
Malice PExecutable Plugin
Identify the processor architecture of binary files
My personal PE Fixer that allows you to patch a raw PE dump to a fully patched and working PE dump that will help your analysis.
This project analyzes PE information of exe files to detect malware. In this repository you will learn how to create your own dataset and will be able to see the use of machine learning models using the dataset. We will use machine learning for detect malware.
Herpaderply Hollowing - a PE injection technique, hybrid between Process Hollowing and Process Herpaderping
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