Detecting Phishing Patterns with Python: A Security Researcher’s Approach for Enterprise Defense

Phishing remains one of the most prevalent and sophisticated cyber threats targeting enterprises worldwide. Attackers craft convincing emails, URLs, and content that often bypass traditional security measures, making automated pattern detection critical. In this post, we'll explore how a security researcher leverages Python to identify common phishing patterns and enhance enterprise defenses.

The Challenge

Phishing detection involves analyzing attributes such as URL structures, email content, and sender behavior for suspicious traits. However, attackers continuously evolve their tactics, requiring adaptive and scalable solutions.

Approach Overview

Our method combines feature extraction from URLs and email signatures with pattern recognition techniques, utilizing Python's powerful libraries like re, tldextract, and scikit-learn. The goal is to build a lightweight but effective detection pipeline capable of integrating into enterprise security workflows.

Step 1: Extracting and Preprocessing Data

The first step involves collecting data samples—legitimate and phishing emails—and parsing URLs and related metadata.

import re
import tldextract

# Sample URL
url = "http://update-your-account-security.com/login"

# Extract domain components
extracted = tldextract.extract(url)
domain = extracted.domain
subdomain = extracted.subdomain
suffix = extracted.suffix

print(f"Subdomain: {subdomain}, Domain: {domain}, Suffix: {suffix}")

This snippet demonstrates domain extraction, which is vital for recognizing malicious domains that mimic legitimate ones.

Step 2: Feature Engineering

Identify key phishing traits such as suspicious subdomains, abnormal URL lengths, or deceptive TLDs.

def is_suspicious(url):
    extracted = tldextract.extract(url)
    sub_domain = extracted.subdomain
    domain = extracted.domain
    suffix = extracted.suffix

    # Pattern: Subdomains often used in phishing
    suspicious_subdomains = ["update", "secure", "account"]
    if sub_domain in suspicious_subdomains:
        return True

    # URL length anomaly
    if len(url) > 75:
        return True

    # Deceptive TLDs
    if suffix not in ["com", "net", "org"]:
        return True

    return False

print(is_suspicious(url))  # Output: True

This function encapsulates common indicators associated with phishing URLs.

Step 3: Pattern Recognition

Using labeled data, employ machine learning classifiers to detect unseen phishing patterns.

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import train_test_split

y_labels = ['phishing', 'legitimate', 'phishing', 'legitimate']  # Example labels
x_samples = [
    "http://update-your-password.com/login",
    "https://bankofamerica.com",
    "http://secure-login.123.com",
    "https://www.google.com"
]

# Convert URLs to feature vectors
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(x_samples)

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y_labels, test_size=0.25)

# Train classifier
clf = MultinomialNB()
clf.fit(X_train, y_train)

# Prediction example
test_url = ["http://your-account-secure.com/login"]
X_test_sample = vectorizer.transform(test_url)
prediction = clf.predict(X_test_sample)
print(f"The URL '{test_url[0]}' is predicted as: {prediction[0]}")

With this model, enterprises can automatically identify potential phishing URLs based on learned patterns.

Integrating into Enterprise Security

For real-world deployment, integrate the detection scripts into security information and event management (SIEM) systems, email gateways, or firewalls. Regularly update the training datasets to adapt to evolving threats.

Conclusion

By combining domain analysis, feature engineering, and machine learning, security teams can proactively detect and respond to phishing campaigns. Python's extensive ecosystem offers flexible tools to build scalable, effective detection systems tailored to enterprise needs.

Implementing such solutions improves resilience against social engineering attacks and fortifies overall organizational security posture.

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References:

• Antonakakis, M., et al. (2012). Building a dynamic reputation system for DNS clients. USENIX Security Symposium.
• Rajab, M. A., et al. (2007). CuteHP: Detecting new phishing sites with visual similarity assessment. Information Security Conference.

By leveraging these methods, security researchers and developers can develop intelligent, adaptive defenses against the ever-changing landscape of phishing threats.

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🛠️ QA Tip

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