Independent Dark Web Data Breach Query with Python
Vijay Gupta
Cyber Security | CEH | CHFI | CYBER Awareness Training | Performance Marketer | Digital Marketing Expert | Podcaster
The Dark Web, often regarded as the underground layer of the internet, hosts various websites that aren’t indexed by regular search engines like Google or Bing. While it has been notorious for illegal activities, it also hosts numerous forums and databases where stolen credentials and sensitive information from data breaches are traded or leaked.
Understanding how to query and monitor these data breaches on the Dark Web can be crucial for cybersecurity professionals, ethical hackers, and individuals looking to protect their online identities. This blog will walk you through an educational guide on how to query the Dark Web for data breaches using Python. Note that this is purely for educational purposes and should not be used for malicious intent. Always comply with the law and ethics.
What Is a Data Breach?
A data breach occurs when unauthorized individuals gain access to confidential data, often including usernames, passwords, credit card numbers, email addresses, and other sensitive information. Many of these breaches find their way to the Dark Web, where they are bought, sold, or freely distributed.
As a cybersecurity professional, learning how to monitor the Dark Web for potential data breaches can help detect if your own or your clients’ data has been compromised.
Why Use Python?
Python is a versatile language that is widely used for automation, data analysis, and security research. With libraries like requests, BeautifulSoup, and Tor proxies, Python can interact with Dark Web search engines, scrape websites, and analyze the data for breaches.
In this tutorial, we’ll build a simple Python script that can:
Disclaimer
Before proceeding, it’s essential to note the following:
Setting Up Your Python Environment
Before starting, make sure you have the necessary tools installed. The main requirements for this tutorial are:
The requests library allows us to send HTTP requests, BeautifulSoup helps parse HTML, and stem is used to communicate with the Tor proxy.
Connecting to the Dark Web Using Python
To access the Dark Web, we need to route our requests through the Tor network. This can be done by setting up a local proxy through which all traffic will be routed.
from stem import Signal
from stem.control import Controller
import requests
# Function to renew Tor identity (change IP)
def renew_connection():
with Controller.from_port(port=9051) as controller:
controller.authenticate(password='your_password') # Your Tor control password
controller.signal(Signal.NEWNYM)# Setup proxy to use Tor network
proxies = {
'http': 'socks5h://127.0.0.1:9050',
'https': 'socks5h://127.0.0.1:9050'
}# Test connection through Tor
def test_tor_connection():
response = requests.get("https://check.torproject.org", proxies=proxies)
if "Congratulations" in response.text:
print("You are connected to the Tor network.")
else:
print("Tor connection failed.")
test_tor_connection()
Make sure to configure the Tor control port and password in the torrc file to enable identity renewal.
Querying Dark Web Search Engines
The next step is to query Dark Web search engines, such as Ahmia or Not Evil, which index .onion sites. These search engines can help find leaked databases and breached information.
Here’s an example of how to search for an email address across the Dark Web using Ahmia.
from bs4 import BeautifulSoup
import requests
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# Ahmia search URL (example)
search_term = "[email protected]" # Replace with the data you want to search for
url = f"https://ahmia.fi/search/?q={search_term}"# Make a request to the Dark Web search engine using Tor proxies
response = requests.get(url, proxies=proxies)# Parse the search results
soup = BeautifulSoup(response.content, 'html.parser')# Find search result links
for result in soup.find_all('a', href=True):
link = result['href']
if ".onion" in link:
print(f"Found .onion link: {link}")
This script will query Ahmia for the provided search term (in this case, an email address) and return Dark Web .onion links where that information might be found.
Scraping Dark Web Pages
After gathering search results, the next step is to visit the .onion pages and scrape them for specific keywords or data. However, scraping the Dark Web can be tricky due to the unpredictable structure of websites. Here’s a simple scraper for educational purposes:
# Visit a .onion page and scrape for data
onion_url = "https://exampleonionsite.onion" # Replace with an actual .onion URL
response = requests.get(onion_url, proxies=proxies)if response.status_code == 200:
soup = BeautifulSoup(response.content, 'html.parser')
# Find specific data patterns, for example email addresses
emails = set()
for word in soup.get_text().split():
if "@" in word and "." in word:
emails.add(word)
# Output found emails
if emails:
print("Found emails:")
for email in emails:
print(email)
else:
print("No emails found.")
else:
print(f"Failed to access {onion_url}")
This basic scraper visits a .onion website and searches for email addresses. It extracts text from the page and looks for the @ symbol and periods, which are common in email addresses.
Analyzing the Data
After scraping the data, you need to analyze it for signs of data breaches. This might involve checking the data against known breach databases, such as HaveIBeenPwned, or simply identifying patterns of compromised credentials.
Basic Data Analysis Example:
# Example: Checking emails against a known breach list
known_breaches = ["[email protected]", "[email protected]"]
def check_breaches(emails):
breached_emails = []
for email in emails:
if email in known_breaches:
breached_emails.append(email)
if breached_emails:
print("Breached emails found:")
for email in breached_emails:
print(email)
else:
print("No known breached emails found.")# Assuming 'emails' contains the scraped emails
check_breaches(emails)
This script checks whether any of the scraped emails are present in a list of known breaches. In practice, you could use APIs like HaveIBeenPwned to automate this.
Using APIs for Dark Web Data Breaches
If you want a more scalable solution, there are several APIs you can use to monitor data breaches. One such API is the HaveIBeenPwned API, which allows you to query whether a particular email or password has been exposed in a breach.
import requests
def check_haveibeenpwned(email):
url = f"https://haveibeenpwned.com/api/v3/breachedaccount/{email}"
headers = {
"hibp-api-key": "your_api_key_here",
"user-agent": "Data Breach Checker"
}
response = requests.get(url, headers=headers)
if response.status_code == 200:
breaches = response.json()
if breaches:
print(f"{email} has been found in the following breaches:")
for breach in breaches:
print(breach['Name'])
else:
print(f"{email} has not been found in any breaches.")
elif response.status_code == 404:
print(f"{email} has not been found in any breaches.")
else:
print(f"Error: {response.status_code}")# Example usage
email_to_check = "[email protected]"
check_haveibeenpwned(email_to_check)
Make sure to sign up for the API and replace "your_api_key_here" with your actual API key.
Conclusion
By following this guide, you’ve learned how to independently query the Dark Web for data breaches using Python. We’ve covered how to:
Remember, this tutorial is solely for educational purposes. Querying the Dark Web and accessing potentially harmful sites or data must be done ethically and in compliance with legal standards. Always ensure you’re protecting your own and others’ privacy and security while conducting such activities.
Additional Resources
Stay safe, stay ethical, and happy coding!
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About the Author:
Vijay Gupta is a cybersecurity enthusiast with several years of experience in cyber security, cyber crime forensics investigation, and security awareness training in schools and colleges. With a passion for safeguarding digital environments and educating others about cybersecurity best practices, Vijay has dedicated his career to promoting cyber safety and resilience. Stay connected with Vijay Gupta on various social media platforms and professional networks to access valuable insights and stay updated on the latest cybersecurity trends.