Mohammad Waseem

Posted on Jan 31

Leveraging Python for Real-Time Phishing Pattern Detection During High Traffic Events

#security #python #phishing

Detecting Phishing Patterns with Python During High Traffic Events

High traffic events such as product launches, flash sales, or major announcements often attract malicious activities, including phishing attacks. Efficient, real-time detection of phishing patterns becomes crucial to safeguard users and maintain system integrity. In this blog post, we'll explore how a security researcher can leverage Python's capabilities for detecting phishing patterns swiftly and accurately during such periods.

The Challenge

High traffic volumes introduce significant noise and increase the difficulty of identifying suspicious activities. Common issues include false positives, increased latency, and resource constraints. Our goal is to create a scalable, lightweight detection system capable of analyzing incoming URLs or email links for signs of phishing, such as lookalike domains, suspicious URL structures, or known malicious patterns.

The Approach

We'll focus on pattern-based detection using regular expressions and domain similarity analysis. Key strategies include:

Blacklist/whitelist filtering
Domain resemblance scoring
Suspicious URL structure detection
Real-time processing with asynchronous programming

Let's start by setting up a Python environment that can handle high throughput.

Setting Up Asynchronous Processing

Python's asyncio library allows us to process multiple data points concurrently without blocking operations. Using aiohttp, we can make asynchronous HTTP requests for domain validation or threat intelligence queries.

import asyncio
import aiohttp

async def check_domain(domain):
    api_url = f"https://api.threatintel.com/v1/domain/{domain}"
    async with aiohttp.ClientSession() as session:
        async with session.get(api_url) as response:
            data = await response.json()
            return data

# Example usage
async def main(domains):
    tasks = [check_domain(domain) for domain in domains]
    results = await asyncio.gather(*tasks)
    return results

This approach enables us to query threat intelligence feeds asynchronously, significantly reducing latency during high traffic.

Pattern Detection with Regular Expressions

Detecting lookalike domains (Typosquatting) is a common phishing tactic. For example, capturing domains that resemble "google.com" but with subtle changes.

import re

def is_suspicious_domain(domain):
    pattern = re.compile(r"(?:g[o0]0g1e|g[o0]0g|g0g|goog1e)\.com")
    return bool(pattern.search(domain))

# Example
print(is_suspicious_domain("g00g1e.com"))  # True
print(is_suspicious_domain("google.com")) # False

More advanced techniques include checking for homoglyphs and character substitution, which can be extended with libraries like fuzzywuzzy for similarity scoring.

from fuzzywuzzy import fuzz

def similar_domains(domain, known_domain):
    similarity = fuzz.ratio(domain, known_domain)
    if similarity > 85:
        return True
    return False

# Example
print(similar_domains("g00g1e.com", "google.com"))  # True

Real-Time Filtering Workflow

Combining these components, here’s a simplified workflow:

Collect incoming URLs during high traffic.
Use regex patterns and fuzzy matching to flag potential phishing domains.
Asynchronously query threat intelligence APIs for contextual risk assessment.
Log suspicious activities and trigger alerts.

async def process_url(url):
    domain = extract_domain(url)
    if is_suspicious_domain(domain) or similar_domains(domain, "google.com"):
        result = await check_domain(domain)
        if result.get("malicious", False):
            alert_user(url, domain)

# Placeholder functions

def extract_domain(url):
    # Implementation to extract domain from URL
    pass

def alert_user(url, domain):
    # Alert mechanism
    print(f"Potential phishing detected for {domain} in URL {url}")

Conclusion

Detecting phishing attacks in high traffic environments necessitates a combination of pattern recognition, asynchronous processing, and threat intelligence integration. Python’s robust ecosystem lends itself well to building scalable, real-time detection systems. Continual refinement of detection algorithms, incorporating machine learning and behavioral analysis, can further enhance security posture in dynamic environments.

References

FuzzyWuzzy Library: https://github.com/seatgeek/fuzzywuzzy
Threat intelligence APIs and resources
Regular expression and domain analysis techniques

Implementing these strategies provides security teams with proactive tools to mitigate phishing threats effectively during critical high traffic periods.

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Pro Tip: Use TempoMail USA for generating disposable test accounts.

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