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Introduction:
Open Source Intelligence (OSINT) has evolved, giving rise to specialized branches like Social Media Intelligence (SOCMINT), which focuses on extracting actionable insights from platforms such as Facebook, Twitter, and Instagram. For security professionals and investigators, mastering tools like the newly surfaced `birdy-edwards-lite` and comprehensive toolkits like the `awesome-osint-arsenal` is critical for gathering data, mapping relationships, and uncovering hidden patterns from publicly available information while maintaining strict legal and ethical boundaries.
Learning Objectives:
- Understand the core features of the `birdy-edwards-lite` tool and its application in Facebook SOCMINT investigations.
- Learn how to deploy and utilize a comprehensive OSINT toolkit on both Linux and Windows environments.
- Explore advanced techniques such as Facebook Graph API enumeration, CNN-based face clustering, and network relationship mapping.
You Should Know:
- Deploying & Mastering Birdy-Edwards Lite for Facebook Investigations
This section expands on the tool’s functionality, covering automated profile intelligence, interaction network graphs, and CNN face clustering. A key feature is its complete independence from AI models, Docker, or cloud services, ensuring local-only operation.
Step-by-Step Guide:
- Prerequisites: Ensure you have a valid Facebook account (for authentication) and Python 3.8+ installed on your local machine.
- Installation: Navigate to the project’s GitHub page and follow the installation instructions. Clone the repository using:
git clone https://github.com/jeet-ganguly/birdy-edwards-lite
- Authentication: The tool requires a valid Facebook session. This is typically handled by exporting your browser’s cookies or using a session string. It operates exclusively using public data accessible to a logged-in user, not private messages or restricted content.
- Basic Usage: Launch the tool’s dashboard. You will initiate an investigation by providing a target Facebook profile URL. The tool will then:
– Automate the collection of posts, photos, comments, and “about” data.
– Run algorithms to generate frequency-scoring and interaction networks.
5. Advanced Analysis:
- Network Mapping: Use the interactive force-directed graph to visualize relationships between a target and their interactors, with frequency-weighted edges.
- Face Clustering: The tool employs CNN/HOG for face detection and 128D encoding to create identity clusters across all gathered images, generating a frequency-cascade “Face cluster tree”.
- Co-interactor Matrix: Analyze heatmaps showing which users interacted together across posts, revealing hidden connections and groups.
- Building Your Ultimate OSINT Arsenal: The One-Command Installer
The `awesome-osint-arsenal` GitHub repository is a curated collection of over 751 OSINT and security tools, covering categories from SOCMINT and GEOINT to network recon and forensics.
Step-by-Step Guide:
- Cloning the Arsenal: On a Debian-based system (like Kali Linux), clone the repository and navigate into it:
git clone https://github.com/rawfilejson/awesome-osint-arsenal cd awesome-osint-arsenal
- Running the Installer: Execute the master installation script. This will install all tools, which may take time and significant disk space:
sudo bash install.sh
- Category-Specific Installation: If you only need tools for a specific task, use the targeted scripts:
– sudo bash osint.sh: Installs only OSINT tools (e.g., Sherlock, Maigret, Amass).
– sudo bash redteam.sh: Deploys red teaming tools like Sliver, BloodHound, and Nuclei.
– sudo bash forensics.sh: Sets up Digital Forensics and Incident Response (DFIR) tools such as Volatility and Ghidra.
4. Verification: After installation, test a simple tool. For example, use `sherlock` to check for a username across hundreds of sites:
sherlock <username_to_check>
- Enumerating the Facebook Graph API: A Command-Line Approach
The Facebook Graph API is a powerful, legitimate source for structured social media data. The `fb-graph-enum` tool allows security professionals to enumerate API endpoints and test token permissions directly from the command line.
Step-by-Step Guide:
- Installation: Ensure you have Go installed, then run:
go install github.com/digilolnet/fb-graph-enum@latest
- Basic App Token Exploration: Use your Facebook App’s Client ID and Secret to explore public data:
./fb-graph-explorer YOUR_CLIENT_ID YOUR_CLIENT_SECRET
- User Token Enumeration: To access user-specific data (with their authorization), generate a User Access Token and use it:
./fb-graph-explorer -token user -permissions "public_profile,email,user_posts" YOUR_CLIENT_ID YOUR_CLIENT_SECRET
- Saving Results: Use the `-save` flag to store all API responses as JSON files for later analysis:
./fb-graph-explorer -save -output ./fb_data YOUR_CLIENT_ID YOUR_CLIENT_SECRET
-
Cross-Platform OSINT & OPSEC: Linux & Windows Commands
Maintaining operational security (OPSEC) is vital. Here are essential commands for both Linux and Windows environments to support your OSINT workflow.
Linux: Harvesting Emails with `theHarvester`
This command-line tool gathers emails, subdomains, and IPs from public sources like search engines.
Step-by-Step Guide:
1. Installation: `sudo apt install theHarvester`
- Execution: Run the tool against a target domain using Google as the source:
theHarvester -d example.com -b google
- Analysis: The output provides a list of discovered email addresses and hosts, which can be used for further intelligence or security assessments.
Windows: Monitoring Network Connections with `netstat`
This built-in utility helps identify suspicious outbound connections and potential data exfiltration.
Step-by-Step Guide:
1. Open Command Prompt as Administrator.
2. Run the command: `netstat -ano`
-a: Displays all active connections and listening ports.-n: Shows addresses and port numbers in numerical form.-o: Displays the owning process ID for each connection.
- Investigation: Review the list of foreign addresses. If any look suspicious, note the PID and end the associated process via Task Manager.
5. Advanced Visual OSINT: From Pixels to People
Modern investigations rely heavily on images. Visual OSINT combines reverse image search, facial recognition, and environmental analysis to track a person’s digital footprint.
Step-by-Step Guide:
- Duplicate & Origin Search: Use TinEye to perform a reverse image search. Its “Oldest” sort can help find the first appearance of an image online, tracking its origin.
- Facial Recognition: Employ tools like PimEyes or FaceCheck.ID. These use biometric algorithms to map facial geometry, allowing you to find a target’s appearance across different photos, even when names are changed.
- Contextual Analysis: For obscured faces or unknown subjects, use Yandex Images, which excels at finding visually similar styles and backgrounds. Analyze the environment, objects, or architecture in the image to narrow down possible locations.
6. Automating Large-Scale SOCMINT with Python Frameworks
For complex investigations, manual searching is not enough. Leveraging Python-based automation frameworks like the `Social-Media-OSINT` tool can streamline data gathering from multiple platforms.
Step-by-Step Guide:
1. Clone & Setup:
git clone https://github.com/ShadowHackrs/Social-Media-OSINT.git cd Social-Media-OSINT pip install -r requirements.txt
2. API Configuration: For enhanced functionality, configure API keys for services like Twitter in a `config.py` file.
3. Run the Tool: Execute the main script to access its interactive menu:
python social_media_tools.py
4. Select a Module: Choose from options like tracking location via social engineering links, finding social media accounts linked to phone numbers, or monitoring activity.
What Undercode Say:
- Key Takeaway 1: The shift from general OSINT to specialized SOCMINT is undeniable. Tools like `birdy-edwards-lite` and the `awesome-osint-arsenal` represent a maturation of the field, moving from simple data collection to sophisticated network analysis and facial clustering.
- Key Takeaway 2: Modern digital investigations are no longer just about text. The integration of CNN-based face clustering and Graph API enumeration in open-source tools signifies a new era where visual data and structured platform data are the primary keys to uncovering online identities and their hidden connections.
Prediction:
As privacy regulations tighten and platforms restrict public data access, SOCMINT will undergo a significant shift. We will see a move towards federated, local-first tools (like birdy-edwards-lite) that empower individual investigators and small teams without reliance on corporate cloud APIs. Furthermore, the convergence of OSINT with generative AI will automate the analysis of massive datasets, but it will also escalate the arms race against sophisticated disinformation and synthetic media, making continuous tool evolution and OSINT skill training absolutely critical for cybersecurity professionals.
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