Email Spoofing Detection Tool – Header Forensics + Domain Auth + ML

1. Introduction

The Email Spoofing Detection Tool is a Python-based cybersecurity project designed to detect and prevent email spoofing attacks. Attackers forge the sender’s address to impersonate trusted sources. This tool analyses headers, validates domain authentication (SPF, DKIM, DMARC), and inspects message content to flag spoofed or suspicious emails. A blend of rule-based checks and ML helps protect users and organizations from fraud and data loss.

2. Existing System vs Proposed System

3. Working

1. Email Input: Upload .eml or paste headers.
2. Header Analysis: Extract sender IP, Return-Path, Message-ID, auth results.
3. Domain Verification: Perform SPF/DKIM/DMARC via DNS.
4. Content Analysis (optional): NLP for phishing cues and mismatched URLs.
5. Classification: ML/heuristics produce Legitimate / Spoofed.
6. Result Display: Detailed reasons + trust score.

4. Technology Stack

• Language: Python
• Libraries: email, dns.resolver, scikit-learn, pandas, re, tkinter/flask
• Algorithms: Random Forest / Naïve Bayes
• Protocols: SPF, DKIM, DMARC validation
• Backend: SQLite3 (logs & reports)
• Interface: CLI or Flask Web UI

5. Modules

6. Advantages

• Accurate detection using header + domain auth.
• Prevents phishing/data theft with validated senders.
• Lightweight, fast, deployable anywhere.
• ML improves accuracy over time.
• Easy enterprise integration for automated protection.

7. Applications

• Corporate/government email security.
• ESPs to harden anti-phishing pipelines.
• Cybersecurity training & awareness labs.
• Forensic analysis for incident response.

Python Integration Sketch (Flask + ML)

# 1) Ingest & Parse
from email import policy
from email.parser import BytesParser

def parse_eml(fp):
    msg = BytesParser(policy=policy.default).parse(fp)
    headers = dict(msg.items())
    body = msg.get_body(preferencelist=('plain','html'))
    return headers, (body.get_content().strip() if body else "")

# 2) SPF/DKIM/DMARC
import dns.resolver
def check_spf(ip, domain): ...
def check_dkim(headers, raw): ...
def check_dmarc(domain): ...

# 3) Heuristics + ML
from sklearn.ensemble import RandomForestClassifier
def extract_features(headers, text): ...
def classify(features):  # Legit / Suspicious / Spoofed + score
    return label, score, reasons

# 4) Flask UI
from flask import Flask, request, render_template
app = Flask(__name__)

@app.route('/scan', methods=['POST'])
def scan():
    eml = request.files['eml'].read()
    headers, text = parse_eml(io.BytesIO(eml))
    spf = check_spf(...)
    dkim = check_dkim(headers, eml)
    dmarc = check_dmarc(...)
    feats = extract_features(headers, text)
    label, score, reasons = classify(feats)
    log_to_sqlite(headers, label, score, reasons)
    return render_template('report.html', label=label, score=score, reasons=reasons, spf=spf, dkim=dkim, dmarc=dmarc)

# 5) SQLite logging & exportable reports
              

What You Get