/AI-driven-phishing-bot

AI-Driven-Phishing-Bot

Understanding the Problem Statement

You need to develop an AI-driven phishing bot that can conduct phishing campaigns. This involves several stages:

  1. Reconnaissance: Collect email addresses from a target domain.

  2. Profiling: Gather detailed information about those email addresses.

  3. Phishing Email Generation: Use AI to create realistic phishing emails.

  4. Communication with Bots: Test the phishing emails on AI-driven bots and track success.

Part 1: Reconnaissance

Objective: Create a dashboard to manage phishing campaigns and gather email addresses from a target domain.

Steps to Implement:

  1. 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.

  1. 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.

  1. 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.

Deliverables:

  • A report on the tools and methods used for reconnaissance.

  • A list of active email addresses collected and verified.

Part 2: Profiling

Objective: Create detailed profiles for each collected email address.

Steps to Implement:

  1. Define Profiling Parameters:
  • Identify parameters to collect (e.g., name, job title, social media profiles, interests).
  1. 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.

  1. Manual Profiling:
  • Allow users to manually input or correct data. Provide an interface where users can upload an Excel sheet or directly edit profiles.

Deliverables:

  • A report on tools and methods used for profiling.

  • Detailed profiles for each active email address.

Part 3: AI-Driven Phishing Email Generation

Objective: Develop an AI model to generate and send phishing emails that appear legitimate.

Steps to Implement:

  1. 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.

  1. 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.

  1. 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.

Deliverables:

  • 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.

Part 4: AI-Driven Bot Communication & Validation

Objective: Develop AI bots to mimic real users and validate the success of phishing attempts.

Steps to Implement:

  1. 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.

  1. 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.

Deliverables:

  • A report on AI model training for bot personas.

  • Samples of bot responses.

  • A report on the phishing campaign success metrics.

Step-by-Step Guide to Start

  1. Set Up Your Environment:

  • Install Python and necessary libraries.

  • Set up a web framework (Flask or Django).

  1. Create the Dashboard:

  • Implement user login and authentication.

  • Create views for managing campaigns.

  • Set up a database (e.g., PostgreSQL).

  1. 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.

  1. Build Profiling System:

  • Define profiling parameters.

  • Develop scripts to gather data from various sources.

  • Create a user interface for manual profiling.

  1. 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.

  1. Execute Phishing Campaigns:

  • Develop a bot to send emails.

  • Track open rates, click rates, and other metrics.

  1. 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.

Tools and Technologies

  • 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