Quick tricks: URL tracking

Today: See the outbound links from a URL using Python and Google Colab

Why you need this:

• Security: spot PHP injections redirecting to fraudulent sites.
• SEO: catch broken links and links to low-quality domains.
• Control: keep your domain safe.

There's a hack where someone injects a malicious file (usually PHP) into your web root.

What is Python?

Language created in 1991 by Guido van Rossum. Accessible, flexible. Three core building blocks:

1. Libraries: ready-made collections of tools.
2. Functions: blocks of code that do one thing.
3. Variables: reusable data.

Basic example

import requests

def fetch_page(url):
    response = requests.get(url)
    return response.text

content = fetch_page("https://www.example.com")
print(content)

What is Google Colab?

Free Google tool for running Python in your browser.

Why it's useful:

• No local setup needed
• Free CPU/GPU
• Real-time collaboration
• Saves to Drive
• Pre-installed libraries (Pandas, NumPy, TensorFlow)

You don't install anything on your computer. All you need is a Google account.

How to use: tracking internal links

Steps:

1. Go to colab.research.google.com
2. Sign in with Gmail
3. File → New notebook
4. Import libraries
5. Define crawler functions

Full code

import requests
from bs4 import BeautifulSoup
from urllib.parse import urlparse, urljoin

domain = "apferrer.com"
visited = set()
internal = set()

def get_links(url):
    links = set()
    try:
        response = requests.get(url)
        soup = BeautifulSoup(response.text, "html.parser")
        for link in soup.find_all('a', href=True):
            link_url = link['href']
            absolute_url = urljoin(url, link_url)
            link_domain = urlparse(absolute_url).netloc
            if not any(x in absolute_url for x in ['mailto:', 'tel:', '#']) and domain in link_domain:
                internal.add(absolute_url)
                links.add(absolute_url)
        return links
    except Exception as e:
        print(f"Error getting links from {url}: {e}")
        return set()

def crawl_site(url):
    if url not in visited:
        visited.add(url)
        links = get_links(url)
        for link in links:
            if link not in visited:
                crawl_site(link)

crawl_site("https://apferrer.com")
print(f"Total internal: {len(internal)}")
print(internal)

That's it

I said it would be quick and practical.

Key Python libraries

1. NumPy - numerical calculations.
2. Pandas - data manipulation.
3. Matplotlib - graphs and charts.
4. Scikit-learn - machine learning.
5. TensorFlow - AI and neural networks.
6. Flask / Django - web frameworks.

Resources

• Colab notebook: https://colab.research.google.com/drive/1sihrcK5Z5FqHHmonAXoQggQ05WfbRZyo

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