bee-san/Ciphey
Ciphey
Ciphey is an automated decryption and data obfuscation tool designed to identify and reverse complex, multi-layered encoding schemes. By utilizing statistical analysis and probability scoring, the system automatically detects unknown data formats and recovers human-readable plaintext from obfuscated input strings without requiring manual algorithm specification.
The tool distinguishes itself through a recursive pipeline that processes nested data structures and strips formatting anomalies or invisible characters to ensure consistent input. It employs a heuristic search and multithreaded execution engine to evaluate multiple decryption paths concurrently, prioritizing those with the highest statistical likelihood of success to resolve obfuscated content efficiently.
Beyond core decryption, the system provides capabilities for cybersecurity incident analysis and forensic examination of suspicious payloads. It includes features for identifying specific data types such as API keys or network addresses, enforcing execution timeouts to maintain predictable performance, and distinguishing valid text from random noise. The software is distributed as a command-line utility for integration into automated data processing workflows.
Features
- Automated Decryption Utilities - Automatically identifies and reverses complex, nested encoding schemes to recover plaintext.
- Obfuscated Data Decoders - Automatically reverses common encoding and encryption schemes to recover human-readable text from obfuscated input.
- Encoding Identification Engines - Uses statistical analysis to detect and categorize unknown data formats without manual specification.
- Incident Investigation Tools - Enables security professionals to decode nested data structures and obfuscated strings during incident response.
- Recursive Decoding Pipelines - Iteratively applies decryption modules to nested data structures until human-readable plaintext is recovered.
- Multi-Layer Decoders - Resolves multi-level, nested encoding structures through automated, recursive conversion steps.
- Data Type Identifiers - Automatically identifies specific data types like API keys or network addresses using pattern matching.
- Decryption Registries - Uses a pluggable registry of independent algorithms to dynamically select the best decryption method for input data.
- Forensic Tools - Cleans malformed data and resolves nested encodings to facilitate forensic examination of suspicious strings.
- Criteria-Based Scoring Engines - Scores potential decryption paths based on the statistical likelihood of producing valid plaintext.
- Encoding Identification - Uses statistical methods to differentiate between valid human-readable text and random noise or encoded data.
- Heuristic Pattern Search Algorithms - Prioritizes decryption paths using statistical probability to efficiently navigate complex, multi-layered encoding schemes.
- Parallel Execution Engines - Employs concurrent processing to evaluate multiple decryption paths simultaneously for faster result identification.
Star history
How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
