Phishing Email Detection System Using NLP and Header Analysis

Project Idea · Advanced · Python, Machine Learning

Problem Statement

Phishing remains the #1 attack vector. Over 90% of successful cyberattacks begin with phishing. Traditional tools rely on blocklists and rules, but sophisticated phishing uses legitimate services, new domains, and social engineering that bypasses rule-based detection.

Proposed Solution

A phishing detection system analyzing emails from two angles: email header forensics (SPF/DKIM/DMARC, routing, domain reputation) and NLP content analysis (urgency, social engineering, links, linguistic anomalies). No blocklist dependency.

Key Features

• Header analysis — SPF, DKIM, DMARC, routing anomalies, domain age
• NLP content analysis — Urgency detection, social engineering patterns
• URL analysis — Link reputation, redirect chains, typosquatting
• Attachment risk scoring — Risky pattern detection
• Confidence scoring — Phishing likelihood with explanation
• Dashboard — Detected threats with analysis breakdown
• API integration — Programmatic email scanning
• Mailbox monitoring — IMAP integration

Implementation Approach

Phase 1: Header Analysis (6-8 weeks)

Python email header parser. SPF/DKIM/DMARC checks. Domain reputation analysis. Rule-based scoring.

Phase 2: NLP Content Analysis (6-8 weeks)

Text feature extraction, urgency/social engineering detection. URL extraction and reputation. Unified scoring model.

Phase 3: Integration & Dashboard (4-6 weeks)

IMAP monitor, React dashboard, user feedback loop, REST API.

Technology Stack

• Python 3.11+ — Core analysis engine
• scikit-learn / PyTorch — ML classifier
• spaCy / NLTK — NLP processing
• FastAPI — REST API
• React — Dashboard

FAQ

What makes this different from existing anti-phishing tools?

Most tools rely on blocklists. This analyzes each email independently using header forensics and NLP — effective against zero-day campaigns.