Mohammad Waseem

Posted on Feb 1

Detecting Phishing Patterns on a Zero-Budget: A Practical Cybersecurity Approach

#python #cybersecurity #phishing

Introduction

Phishing remains one of the most prevalent attack vectors in cybersecurity, targeting both individuals and organizations. Detecting phishing patterns effectively, especially without a budget for advanced tools, requires ingenuity and leveraging existing resources. In this post, we'll explore a zero-cost approach for identifying common phishing indicators using open-source tools, Python scripting, and public data sources.

Understanding Phishing Patterns

Phishing attacks often share recognizable traits:

Suspicious URLs with misspellings or strange domains
Use of HTML forms designed to steal credentials
Email headers indicating spoofing
Use of embedded images or obfuscated scripts While comprehensive detection may require complex machine learning models, basic pattern recognition can be quite effective and accessible.

Gathering Data

A critical step is collecting data for analysis. Without a budget, you can rely on open-source feeds and community reports.

Use publicly available blacklists such as PhishTank
Collect recent phishing URLs from sources like APWG
Analyze email headers if samples are available.

Here's an example of downloading and processing phishing URLs from PhishTank:

import requests

def fetch_phishing_urls():
    url = 'https://data.phishtank.com/data/online-valid.csv'
    response = requests.get(url)
    lines = response.text.splitlines()
    urls = []
    for line in lines[1:]:  # Skip header
        parts = line.split(',')
        if len(parts) > 2:
            urls.append(parts[2])  # URL field
    return urls

phishing_urls = fetch_phishing_urls()
print(f"Fetched {len(phishing_urls)} phishing URLs")

Pattern Detection Techniques

Since we can't afford commercial tools, we focus on simple heuristics:

Domain analysis
URL syntax inspection
WHOIS data comparison

Example: Detect Suspicious Domains

from urllib.parse import urlparse
import tldextract

def is_suspicious_domain(url):
    parsed = urlparse(url)
    domain = parsed.netloc
    ext = tldextract.extract(domain)
    # Check for strange TLDs or misspellings
    suspicious_tlds = ['.xyz', '.top', '.club', '.tk']
    if ext.suffix in suspicious_tlds:
        return True
    # Further checks can include length, odd characters, etc.
    if len(domain) > 20:
        return True
    return False

for url in phishing_urls:
    if is_suspicious_domain(url):
        print(f"Suspicious domain detected: {url}")

URL Pattern Checks

Look for obfuscated URLs, excessive parameters, or encoded characters.

import re

def is_obfuscated_url(url):
    # Detect URL encoding or hex characters
    if re.search(r'%[0-9A-Fa-f]{2}', url):
        return True
    if len(re.findall(r'//', url)) > 1:
        return True
    return False

for url in phishing_urls:
    if is_obfuscated_url(url):
        print(f"Obfuscated URL pattern found: {url}")

Putting It All Together

By combining URL analysis, suspicious domain detection, and pattern heuristics, you can build a lightweight, rule-based phishing detector.

Example: Simple Detection Script

def is_potential_phish(url):
    if is_suspicious_domain(url) or is_obfuscated_url(url):
        return True
    return False

for url in phishing_urls:
    if is_potential_phish(url):
        print(f"Possible phishing URL: {url}")

Conclusion

While no approach guarantees 100% detection, leveraging open data sources, Python scripting, and straightforward heuristics can yield an effective, zero-budget system for identifying common phishing patterns. Regular updates to your heuristics, community-sourced data, and continuous refinement are essential for maintaining effectiveness.

Being resourceful and understanding behavioral patterns in phishing are crucial skills. Though simple, these methods form a foundational layer in offensive and defensive cybersecurity strategies, adaptable to evolving threats.

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