README.md

---

language:
  - en
license: mit
library_name: phishbyte
pipeline_tag: text-classification
tags:
  - phishing-detection
  - email-security
  - cybersecurity
  - security
  - pytorch
  - from-scratch
  - no-pretrained-weights
  - cascading-inference
  - lightweight
  - explainable-ai
  - nlp
  - phishing
  - spam-detection
  - malware-detection
  - threat-detection
  - email-classification
  - text-classification
  - feature-engineering
  - interpretable-ml
  - tfidf
  - residual-network
  - cross-signal-fusion
  - lexical-analysis
  - calibrated-probabilities
datasets:
  - ceas-2008
  - enron-email
  - spamassassin
  - ling-spam
  - nazario-phishing
  - nigerian-fraud
metrics:
  - f1
  - precision
  - recall
  - accuracy
model-index:
  - name: phishbyte
    results:
      - task:
          type: text-classification
          name: Phishing Email Detection
        dataset:
          name: 7-corpus benchmark (CEAS, Enron, SpamAssassin, Ling-Spam, Nazario, Nigerian, farshad72)
          type: ceas-2008
        metrics:
          - type: f1
            value: 0.9445
            name: F1 Score
          - type: accuracy
            value: 0.9470
            name: Accuracy
          - type: precision
            value: 0.9369
            name: Precision
          - type: recall
            value: 0.9523
            name: Recall
widget:
  - text: "From: PayPal Security <security@paypa1-alert.tk>\nReply-To: attacker@evil-domain.ru\nSubject: URGENT: Your account will be suspended\n\nDear Customer, your PayPal account has been suspended. Verify now at http://paypal-login.tk/verify"
    example_title: "Phishing email example"
  - text: "From: alice@company.com\nReply-To: alice@company.com\nSubject: Team lunch tomorrow\n\nHi everyone, lunch is at noon in the usual spot. See you there!"
    example_title: "Legitimate email example"
---


# Phish_Byte v8



A from-scratch PyTorch model for **email phishing detection** — no pretrained weights, no transformers, no fine-tuning.



**F1 0.944** on 5,000 held-out samples from a 7-corpus, 166K-email benchmark.

**716K parameters** (~90× smaller than DistilBERT).

**815 emails/sec** on a laptop GPU.

**104 engineered features** across 8 analysis modules.

**Temperature-calibrated confidence** — probability outputs are empirically calibrated, not just monotonic scores.

Every verdict explains itself with full per-feature attribution.



> The only non-transformer phishing detection model on HuggingFace.



---



## ⚠️ Install — no PyPI package yet



`pip install phishbyte` does **not** work. Clone the source repository:



```bash

git clone https://github.com/AnonymousSingh-007/Phish_Byte.git
cd Phish_Byte

python -m venv venv && source venv/bin/activate   # Windows: .\venv\Scripts\Activate.ps1

pip install -r requirements.txt

python verify_install.py    # confirms everything before you start
```



Then from inside the cloned folder:



```python

from phishbyte import PhishByteEngine



engine  = PhishByteEngine.from_pretrained("SamSec007/phishbyte")

verdict = engine.analyze(raw_email_string)



print(verdict.label)             # "phishing" or "legitimate"

print(verdict.probability)       # calibrated P(phish) in [0.0, 1.0]

print(verdict.confidence)        # "high" / "medium" / "low"

print(verdict.layer_used)        # 1 = rules decided, 2 = MLP decided

print(verdict.feature_weights)   # all 104 signal values

```

`from_pretrained()` downloads ~3 MB (weights + thresholds + TF-IDF vocab) and caches locally. Every call after the first is instant.

---

## Analyse a real email from Gmail

1. Open the email → **⋮** → **Show original**
2. Select all (Ctrl+A), copy (Ctrl+C)
3. Run `python cli.py`, paste when prompted, press Enter then Ctrl+Z (Windows) or Ctrl+D (Mac/Linux)

Or save as `.eml`:

```bash

python cli.py --file suspicious.eml

```

---

## What changed in v8 vs v7

| | v7 | v8 |
|---|:---:|:---:|
| Parameters | 254K | **716K** |
| Features | 85 | **104** |
| Architecture | 85→360→180×2→90→48→1 | **104→620→310×2→155→76→1** |
| Training corpus | 83K emails, 6 sources | **166K emails, 7 sources** |
| F1 (held-out) | 0.950 | **0.944** |
| Cross-signal fusion | None | **Yes — 5 inter-module features** |
| Lexical domain analysis | None | **Yes — character-level on sender + link domain** |
| BDI features | 3 | **5 (+ IP-target forms, open redirects)** |
| Confidence calibration | Post-hoc threshold | **Learned temperature parameter (Platt scaling)** |
| Training metric | Naive 0.5 cutoff | **Youden-optimal threshold** |

The F1 drop from 0.950 to 0.944 reflects a harder benchmark — the corpus doubled in size and the evaluation pool now includes modern notification-style legitimate emails (CNN news digests, mailing lists, marketing email) that the 83K model never saw.

---

## How it works — plain language

Eight independent analysis modules run on every email:

1. **Domain analysis** — checks whether From, Reply-To, and Return-Path addresses are internally consistent; detects display-name spoofing (e.g. "PayPal Security" sending from an unrelated domain); flags suspicious domain patterns
2. **URL and body analysis** — HTTPS/HTTP ratio, anchor text vs href mismatch, urgency language normalized per 100 words, link density by unique destinations
3. **SPF validation** — live DNS lookup to verify the sending server is authorized
4. **Subject line analysis** — urgency, currency, brand names, ALL-CAPS, fake RE: prefixes
5. **Body Domain Identification (BDI)** — finds the most common link destination domain; flags form actions pointing at raw IP addresses; detects open-redirect URL patterns
6. **Lexical domain analysis** — character-level forensics on the sender domain AND the most-linked domain: digit runs, hyphen stacking, Shannon entropy, typosquat distance to known brands (leet-normalized: `paypa1` → `paypal`)
7. **Cross-signal fusion** — computes interaction features across all six modules: trust consistency (SPF pass + domain agreement + BDI match), multi-module agreement score, domain/BDI compounding, lexical brand confusion
8. **TF-IDF vocabulary** — 50 discriminative unigrams learned from the training corpus (no pretrained LM)

All 104 outputs concatenate into one vector and feed a residual MLP with a learned temperature scalar for calibrated confidence.

---

## Architecture

```

raw email

  → 8 analysis modules → 104-dim feature vector

  → Layer 1 gate: composite score ≥ 0.85 → fast PHISHING verdict

  → Layer 2: residual MLP

      104 → 620 → 310 (×2 ResBlock) → 155 → 76 → 1

      + input-to-output skip connection

      + learned temperature scalar (Platt scaling)

  → calibrated P(phish) + PhishVerdict with 104-feature attribution

```

---

## Benchmarks

Evaluated on 5,000 held-out samples, self-reported.

| Metric | Phish_Byte v8 | DistilBERT fine-tuned\* |

|--------|:------------:|:---------------------:|

| F1 score | 0.944 | ~0.967 |

| Accuracy | 94.70% | ~97% |

| Parameters | **716K** | 66,000,000 |

| Model size | **~3 MB** | ~263 MB |

| Throughput (GPU) | **815/sec** | ~50/sec |

| GPU required | **No** | Practically yes |

| Header + SPF analysis | **Yes** | No |

| Per-feature attribution | **104 features** | Token-level SHAP |

| Confidence calibrated | **Yes (temperature)** | No |



\* Self-reported by a different author on a different split. Not apples-to-apples — treat both F1 numbers as directional.



---



## Feature groups (104 total)



| Group | Count | What it captures |

|-------|:-----:|-----------------|

| Domain | 7 | header consistency, brand impersonation, display-name spoof, suspicious pattern |

| URL + Body | 10 | link security, anchor mismatch, urgency, caps ratio, digit ratio |

| SPF | 3 | live DNS sender authorization |

| Subject | 7 | urgency, security theme, brand, currency, caps, fake RE, fake txn ID |

| BDI | 5 | MCLD mismatch, form action mismatch, external link ratio, IP-target form, open redirect |

| Lexical — sender domain | 6 | digit runs, hyphen runs, entropy, vowel anomaly, typosquat distance, length |

| Lexical — most-linked domain | 6 | same six on the dominant link destination |

| Cross-signal fusion | 5 | trust consistency, SPF-pass URL discount, multi-module agreement, domain/BDI compounding, lexical brand confusion |

| TF-IDF | 50 | top-50 discriminative unigrams from training corpus |

| Composite | 5 | per-module summary scores |



---



## Training corpus (166K emails, 7 sources)



| Source | Emails |

|--------|-------:|

| CEAS-2008, Enron, SpamAssassin, Ling-Spam, Nazario, Nigerian Fraud | ~83K |

| farshad72/spam_email (HuggingFace) | ~83K |
| **Total after dedup** | **~166K** |

Balance: ~56% phishing / 44% legitimate.

---

## Troubleshooting

Run `python verify_install.py` first — it identifies the exact problem rather than a confusing traceback.

| Error | Fix |
|-------|-----|
| `ModuleNotFoundError: No module named 'phishbyte'` | Not in cloned folder or venv not activated |
| `ImportError: cannot import name 'X'` | `git pull origin main` |
| `pip install phishbyte` fails | No PyPI package yet — clone the repo |
| Model download hangs | Check internet — Hub: huggingface.co/SamSec007/phishbyte |
| Windows symlink warning | Harmless — ignore or enable Developer Mode |

---

## Limitations — read before deploying

- **Most training data predates 2010.** Modern phishing (OAuth abuse, QR lures, redirect chains through legitimate cloud services) is underrepresented. Recall on 2020s attacks is not independently verified.
- **No DMARC feature yet.** Emails sent via legitimate ESPs (Marketo, Mailgun, SendGrid) will trigger `spf_fail` even when `dmarc=pass`. This causes false positives on marketing email from large organizations. DMARC extraction is the next planned feature.
- **No adversarial robustness testing.** Use as one signal in defence-in-depth, not a standalone gate.
- **F1 0.944 is self-reported** on a held-out split of the training corpus.
- **English-language only.**

---

## Roadmap

- [ ] DMARC feature — fixes false positives on ESP-delivered legitimate email
- [ ] Retrain on 2020–2024 phishing data (PhishTank, OpenPhish, APWG eCrime)
- [ ] HuggingFace Space demo (try in-browser, zero install)
- [ ] PyPI package (`pip install phishbyte`)
- [ ] arXiv preprint
- [ ] Adversarial robustness test suite

## Citation

```bibtex

@software{phishbyte2026,

  author = {Singh, Samratth},

  title  = {Phish_Byte: Cascading from-scratch PyTorch phishing detection},

  year   = {2026},

  url    = {https://github.com/AnonymousSingh-007/Phish_Byte}

}

```

## License

MIT