7 Best Python Digital Forensics Libraries in 2023

Python Digital Forensics Libraries are Python modules, functions, and script collections. It offers capabilities and tools for forensic investigators to analyze digital evidence. These libraries offer various features for helping investigators. It offers various aspects of digital forensics. It includes memory forensics, malware analysis, and file system analysis.  

These libraries offer tools for analyzing file systems and disk images. It will allow investigators to examine directories, files, and other data stored. These libraries offer tools for analyzing the memory of a memory dump or a live system. It will allow investigators to extract information. It helps with information about network connections, running processes, and other system data. These libraries provide tools for analyzing binary files. It will allow us to disassemble and analyze malware and other malicious code. These libraries provide tools for analyzing network traffic. It will allow us to capture and examine packets for evidence. We have to check about evidence of malicious activity or data exfiltration. These libraries offer tools for analyzing and decrypting encrypted communications and data. These offer tools for recovering deleted files and other data. 

Here are the 7 best Python Digital Forensics Libraries handpicked for developers:

beagle:

• Is an open source library that offers incident response and digital forensics tools. 
• Is designed to help investigators automate common forensic tasks and analyze large data. 
• Offers tools for analyzing disk images and file systems. 
• Allow us to examine the system's directories, files, and other data.

Digital-Forensics-Guide:

• Is a Python package that offers tools for incident response and digital forensics.  
• Includes memory forensics, malware analysis, file system analysis, and network analysis. 
• Includes notebooks and scripts demonstrating how to analyze disk images and file systems.
• Offers various techniques and tools.
• Offers tools for analyzing digital evidence and identifying potential indicators of compromise.

ThePhish: 

• Is an automated phishing email analysis tool based on MISP, TheHive, and Cortex. 
• Automates the entire analysis process starting from the extraction of the observables. 
• Will start from the header to the body of an email to the elaboration of a final verdict in most cases. 
• Allows the analyst to intervene in the analysis process and get further details. 

dfirtrack:

• Is a web application designed for Digital Forensics and Incident Response teams.
• It will help manage and track the progress of their investigations.
• Offers a centralized platform for managing different investigations.
• Supports investigations like case updating, closing, and creation.
• Enables you to track and manage all digital evidence related to a particular case.
• Track evidence like associated metadata and storage locations.

Cortex-Analyzers:

• Offers a collection of analyzers for use with Cortex and TheHive platforms. 
• Is a collaborative incident response platform for tracking and managing security incidents. 
• Helps analyze file types, identify potential threats, and extract metadata. 
• Helps analyze and identify malicious activity, detect data exfiltration, and analyze network traffic.

Forensic-Tools:

• Used for parsing Firefox profile databases.
• Can help extract cookies, Google searches, and history.
• Used for analyzing Facebook app and messenger, still new and currently tested.
• Can extract messages with links, contacts, time, and attachments.
• Helps with profile pictures and links. 
• Can extract account details, call logs, messages, and contacts with their full details. 

kobackupdec:

• Is a Python library for decrypting backups.
• Can be created by the KNOX security feature on Samsung devices.
• Allows forensic investigators to extract data from encrypted backups.
• Enables them to perform digital forensics analysis on the extracted data.
• Uses a brute-force approach to decrypt the encrypted backup files.