AI-Based Phishing Email Detector & Analyzer · Hacking Project

Accurately classify inbound emails as phishing/benign with explainable, header-to-content evidence and extracted indicators.

**The problem**
Phishing remains the top initial-access vector, and generative AI now produces grammatically flawless, highly personalized lures that defeat legacy signature and keyword filters. Enterprises need detection that understands intent, brand impersonation, and social-engineering pressure — not just known-bad indicators. Business Email Compromise (BEC) and credential-harvesting campaigns cause billions in annual losses.

**What you'll build**
Build an ML/NLP pipeline that parses raw email (headers, body, URLs, attachments) and scores phishing likelihood using combined signals: authentication results (SPF/DKIM/DMARC), URL/domain reputation and lookalike detection, header anomaly analysis, and a transformer-based text classifier trained on intent and social-engineering cues. Provide an analyst dashboard that explains verdicts and extracts IOCs for downstream blocking.

**Objective** — Accurately classify inbound emails as phishing/benign with explainable, header-to-content evidence and extracted indicators.

**Expected outcome** — A working detector (API + dashboard) that ingests .eml files, returns a scored verdict with contributing signals, and exports IOCs (URLs, domains, hashes) in STIX/JSON.

**Difficulty** — Intermediate — mainstream ML/NLP plus email-protocol knowledge; advanced if adding adversarial-robustness and live inference.

**Primary category** — SOC / Email Security; Secondary = AI/ML Security, Threat Detection

**Security domains** — Primary = Blue Team; Secondary = AI Security, Threat Intelligence, Incident Response

**Technical focus** — NLP text classification, transformer fine-tuning, URL/domain reputation, homoglyph/lookalike detection, email header forensics, SPF/DKIM/DMARC analysis, IOC extraction, feature engineering, explainable AI

**Core technologies** — Languages: Python; Frameworks/Libraries: scikit-learn, Hugging Face Transformers (BERT/DistilBERT), spaCy, email/mailparser, tldextract; Security Tools: VirusTotal API, URLScan, PhishTank feed, YARA (attachments); Cloud/Infra: Docker, serverless inference; AI/Data: transformer embeddings, LLM for intent classification; Databases: PostgreSQL, Elasticsearch (IOC store)

**Where it's valued** — All enterprises, MSSPs/SOCs, email-security vendors, finance, healthcare, government

**Careers it builds toward** — SOC Analyst (Tier 1/2), Email Security Engineer, Detection Engineer, AI Security Engineer, Threat Analyst

**Framework alignment**
• MITRE ATT&CK: Phishing (T1566), Spearphishing Link (T1566.002), Spearphishing Attachment (T1566.001); defends against Initial Access (TA0001). ATLAS relevant if adversary uses LLM-generated lures.
• NIST CSF 2.0: Detect (anomaly & malicious-content detection), Protect (email filtering), Respond (IOC extraction feeds response)
• OWASP: LLM Top 10 — LLM01 Prompt Injection awareness if analyzer uses an LLM on attacker-controlled text; API security for the detection endpoint
• CIS Controls v8: Control 9 (Email and Web Browser Protections), Control 10 (Malware Defenses), Control 13 (Network Monitoring)

**Innovation** — Improved Existing Solution — commercial gateways exist, but an explainable, GenAI-aware, open detector with intent classification is a strong portfolio and product angle. Scores: Innovation 6/10, Startup 7/10, Enterprise-Readiness 7/10, Patent = Possible.