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A curated list of awesome resources for executable packing, unpacking, and detection, covering packers, tools, and literature.
A curated list of awesome resources, tools, and literature on executable packing, unpacking, and detection for malware analysis and cybersecurity.
A curated list of awesome resources (papers, tools, packers) related to executable packing, unpacking, and detection for malware analysis and cybersecurity.
A packer that compresses and encrypts .NET assemblies, storing payloads within PE file structures.
Python implementation of PEiD for detecting packers in Windows PE files using signature databases.
A simple Windows EXE packing tool for learning and investigating PE file structure.
A PE packer that compresses executables with Huffman encoding and encrypts them with XOR, executing directly from memory.
Docker container with a CLI toolkit for generating datasets of packed executables and training ML models for packing detection.
A Windows x86 PE packer written in pure C/C++ that implements a linker for repackaging executables with in-memory decompression.
A curated dataset of packed and unpacked PE executables for training machine learning models to detect packing.
Encrypts 64-bit ELF files with self-decryption at runtime for binary protection.
An ELF packer that encrypts the .text section of executables and injects self-decryption shellcode, printing '....WOODY....' upon execution.
A dataset of ELF files packed with various packers for training machine learning models on executable packing detection.
Adversarial tool that alters packed executables to evade static packing detection by malware analysis tools.
A detailed guide and implementation for a simple PE file packer that encrypts executable sections and adds an unpacking stub.
A PE file packer that adds a custom shell to executable files for protection and modification.
An extensible ELF64 packer for Linux x86_64 and AArch64, featuring 22 compression codecs, EXE filters, and ML-driven codec selection.
Detects packers in executable files using an entropy-based heuristic from academic research.
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