You need to develop an AI-driven phishing bot that can conduct phishing campaigns. This involves several stages:
-
Reconnaissance: Collect email addresses from a target domain.
-
Profiling: Gather detailed information about those email addresses.
-
Phishing Email Generation: Use AI to create realistic phishing emails.
-
Communication with Bots: Test the phishing emails on AI-driven bots and track success.
Objective: Create a dashboard to manage phishing campaigns and gather email addresses from a target domain.
- Dashboard Setup:
-
Create a web application with user login functionality. This can be done using frameworks like Flask (Python), Django (Python), or even a frontend framework like React with a backend server.
-
Set up a database to store campaigns and email addresses. Use a relational database like PostgreSQL or MySQL.
- Campaign Management:
-
Create a user interface to create new campaigns and view existing ones.
-
Allow users to input a target domain name for the campaign.
- Email Collection:
-
Automated Collection: Write scripts to scrape email addresses from the web. Tools like BeautifulSoup and Scrapy in Python are useful for web scraping.
-
Example tools and techniques: Google Dorking, Hunter.io API.
-
Email Verification: Use APIs (e.g., NeverBounce, ZeroBounce) to verify active email addresses.
-
CSV Upload: Allow users to upload a CSV file with email addresses. Process this file to filter and store unique email addresses.
-
A report on the tools and methods used for reconnaissance.
-
A list of active email addresses collected and verified.
Objective: Create detailed profiles for each collected email address.
- Define Profiling Parameters:
- Identify parameters to collect (e.g., name, job title, social media profiles, interests).
- Automated Profiling:
-
Use APIs and scraping tools to gather data. For example, use LinkedIn API, Facebook Graph API, or general web scraping.
-
Store this data in your database.
- Manual Profiling:
- Allow users to manually input or correct data. Provide an interface where users can upload an Excel sheet or directly edit profiles.
-
A report on tools and methods used for profiling.
-
Detailed profiles for each active email address.
Objective: Develop an AI model to generate and send phishing emails that appear legitimate.
- Training the Model:
-
Collect a dataset of spam and legitimate emails.
-
Use this dataset to train a language model (e.g., GPT-4) to generate realistic phishing emails.
-
Fine-tune the model to understand and replicate legitimate email patterns.
- Phishing Email Creation:
-
Generate emails tailored to the profiles created in Part 2. The content should be convincing and contain phishing links or attachments.
-
Use natural language processing (NLP) techniques to ensure the emails appear legitimate.
- Phishing Campaign Execution:
-
Develop a bot to send the phishing emails to the collected email addresses.
-
Use email-sending libraries like smtplib in Python or email services like SendGrid.
-
A report on the AI model training process and the dataset used.
-
Samples of generated phishing emails.
-
A report on the phishing campaign's performance metrics.
Objective: Develop AI bots to mimic real users and validate the success of phishing attempts.
- Bot Persona Creation:
-
Create AI personas based on the profiled data from Part 2.
-
Train models to mimic real user behavior, including language style and response patterns.
- Phishing Attempt Communication & Validation:
-
Send phishing emails to these AI personas.
-
Evaluate their responses to determine if the phishing attempt was successful.
-
If the attempt fails, adjust the email content and try again.
-
A report on AI model training for bot personas.
-
Samples of bot responses.
-
A report on the phishing campaign success metrics.
- Set Up Your Environment:
-
Install Python and necessary libraries.
-
Set up a web framework (Flask or Django).
- Create the Dashboard:
-
Implement user login and authentication.
-
Create views for managing campaigns.
-
Set up a database (e.g., PostgreSQL).
- Develop Reconnaissance Tools:
-
Write web scraping scripts using BeautifulSoup and Scrapy.
-
Integrate Hunter.io API for email collection.
-
Implement email verification using APIs like NeverBounce.
- Build Profiling System:
-
Define profiling parameters.
-
Develop scripts to gather data from various sources.
-
Create a user interface for manual profiling.
- Train AI Model for Phishing Emails:
-
Collect and preprocess a dataset of emails.
-
Train a language model (e.g., GPT-4) using this dataset.
-
Generate sample phishing emails.
- Execute Phishing Campaigns:
-
Develop a bot to send emails.
-
Track open rates, click rates, and other metrics.
- Create and Test AI Personas:
-
Develop AI-driven bots to mimic real users.
-
Send phishing emails to these bots and analyze responses.
-
Iterate and improve email content based on bot feedback.
-
Web Framework: Flask, Django
-
Database: PostgreSQL, MySQL
-
Web Scraping: BeautifulSoup, Scrapy
-
Email Verification: NeverBounce, ZeroBounce
-
NLP and AI: GPT-4, TensorFlow, PyTorch
-
Email Sending: smtplib, SendGrid