Detecting Phishing Patterns in Enterprise Environments Using Python
As enterprises face an increasing volume of sophisticated phishing attacks, the need for robust, scalable, and intelligent detection systems becomes paramount. In this blog post, we implement a high-level approach to detecting phishing patterns leveraging Python, combining machine learning techniques with pattern analysis to proactively identify malicious emails and URLs.
Understanding the Challenge
Phishing attacks often rely on subtle social engineering tactics, malicious URLs, and email spoofing. Effective detection requires analyzing email metadata, content, and external links for patterns aligning with known phishing tactics.
Core Strategy
Our approach involves:
- Collecting email and URL data.
- Extracting meaningful features.
- Using machine learning classifiers trained on labeled datasets.
- Deploying pattern matching for known phishing signatures.
Step 1: Data Collection
For simplicity, assume we have a CSV with email data, including fields like sender, subject, body, and url.
import pandas as pd
df = pd.read_csv('enterprise_emails.csv')
# Sample data frame structure
# sender,subject,body,url
Step 2: Feature Extraction
Extract features such as URL length, presence of suspicious domains, encodings, and email metadata.
import re
from urllib.parse import urlparse
def extract_url_features(url):
features = {}
parsed = urlparse(url)
features['url_length'] = len(url)
features['domain'] = parsed.netloc
features['path_length'] = len(parsed.path)
features['has_https'] = 1 if parsed.scheme == 'https' else 0
# Suspicious TLDs or domains
suspicious_domains = ['secure-login.com', 'verify-account.net', 'update-info.org']
features['suspicious_domain'] = 1 if parsed.netloc in suspicious_domains else 0
return features
# Applying to dataset
url_features_list = []
for url in df['url']:
url_features_list.append(extract_url_features(url))
df_url_features = pd.DataFrame(url_features_list)
Step 3: Model Training
Using labeled datasets, train a classifier such as Random Forest to distinguish phishing from legitimate traffic.
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
labels = df['label'] # Assume binary labels: 1 for phishing, 0 for legitimate
features = df_url_features
X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)
clf = RandomForestClassifier(n_estimators=100, random_state=42)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print(classification_report(y_test, y_pred))
Step 4: Pattern Matching and Signature Detection
Beyond machine learning, pattern matching for known phishing signatures enhances detection.
# Define common phishing patterns
phishing_patterns = [r'verify[-_ ]?account', r'password[-_ ]?reset', r'urgent[-_ ]?action']
def match_phishing_patterns(text):
for pattern in phishing_patterns:
if re.search(pattern, text, re.IGNORECASE):
return True
return False
# Check email bodies or URLs for patterns
for index, row in df.iterrows():
if match_phishing_patterns(row['body']) or match_phishing_patterns(row['url']):
print(f"Potential phishing detected in email from {row['sender']}")
Conclusion
Combining machine learning with signature-based pattern matching offers a comprehensive solution for enterprise phishing detection. While ML models improve adaptability, signature detection provides immediate recognition of known threats. Deploying such systems in production involves continuous model retraining, signature updating, and integration with enterprise email gateways.
Implementing these techniques enhances the security posture by proactively identifying and mitigating phishing attempts, protecting organizational assets and data integrity.
Tags: python, security, enterprise
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