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This License applies\nseparately for each version of the Licensed Work and the Change Date may vary\nfor each version of the Licensed Work released by Licensor.\n\nYou must conspicuously display this License on each original or modified copy\nof the Licensed Work. If you receive the Licensed Work in original or\nmodified form from a third party, the terms and conditions set forth in this\nLicense apply to your use of that work.\n\nAny use of the Licensed Work in violation of this License will automatically\nterminate your rights under this License for the current and all other\nversions of the Licensed Work.\n\nThis License does not grant you any right in any trademark or logo of\nLicensor or its affiliates (provided that you may use a trademark or logo of\nLicensor as expressly required by this License).\n\nTO THE EXTENT PERMITTED BY APPLICABLE LAW, THE LICENSED WORK IS PROVIDED ON\nAN \"AS IS\" BASIS. 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However, the Licensed Work will eventually be made available\nunder an Open Source License, as stated in this License.\n\nFor more information on the use of the Business Source License for MariaDB\nproducts, please visit the MariaDB Business Source License FAQ at\nhttps://mariadb.com/bsl-faq-mariadb.\n\nFor more information on the use of the Business Source License generally,\nplease visit the Adopting and Developing Business Source License FAQ at\nhttps://mariadb.com/bsl-faq-adopting.\n\n-----------------------------------------------------------------------------\n\nBusiness Source License 1.1\n\nTerms\n\nThe Licensor hereby grants you the right to copy, modify, create derivative\nworks, redistribute, and make non-production use of the Licensed Work. The\nLicensor may make an Additional Use Grant, above, permitting limited\nproduction use.\n\nEffective on the Change Date, or the fourth anniversary of the first publicly\navailable distribution of a specific version of the Licensed Work under this\nLicense, whichever comes first, the Licensor hereby grants you rights under\nthe terms of the Change License, and the rights granted in the paragraph\nabove terminate.\n\nIf your use of the Licensed Work does not comply with the requirements\ncurrently in effect as described in this License, you must purchase a\ncommercial license from the Licensor, its affiliated entities, or authorized\nresellers, or you must refrain from using the Licensed Work.\n\nAll copies of the original and modified Licensed Work, and derivative works\nof the Licensed Work, are subject to this License. This License applies\nseparately for each version of the Licensed Work and the Change Date may vary\nfor each version of the Licensed Work released by Licensor.\n\nYou must conspicuously display this License on each original or modified copy\nof the Licensed Work. If you receive the Licensed Work in original or\nmodified form from a third party, the terms and conditions set forth in this\nLicense apply to your use of that work.\n\nAny use of the Licensed Work in violation of this License will automatically\nterminate your rights under this License for the current and all other\nversions of the Licensed Work.\n\nThis License does not grant you any right in any trademark or logo of\nLicensor or its affiliates (provided that you may use a trademark or logo of\nLicensor as expressly required by this License).\n\nTO THE EXTENT PERMITTED BY APPLICABLE LAW, THE LICENSED WORK IS PROVIDED ON\nAN \"AS IS\" BASIS. 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\n \"spamscanner\"\n

\n
\n \"build\n \"code\n \"styled\n \"made\n \"license\"\n
\n
\n
\n Spam Scanner is the best anti-spam, email filtering, and phishing prevention service.\n
\n
\n
\n Spam Scanner is a drop-in replacement and the best alternative to SpamAssassin, rspamd, SpamTitan, and more.\n
\n
\n\n> \\[!NOTE]\n> Spam Scanner is actively maintained and used in production at [Forward Email](https://forwardemail.net) to protect millions of emails daily.\n\n\n## Table of Contents\n\n* [Foreword](#foreword)\n* [Installation](#installation)\n * [npm (Recommended)](#npm-recommended)\n * [Standalone Binary (No Node.js Required)](#standalone-binary-no-nodejs-required)\n * [ClamAV Installation (Optional)](#clamav-installation-optional)\n* [CLI (Command Line Interface)](#cli-command-line-interface)\n * [CLI Installation](#cli-installation)\n * [Commands](#commands)\n * [Exit Codes](#exit-codes)\n * [CLI Examples](#cli-examples)\n* [Why Spam Scanner](#why-spam-scanner)\n * [Key Advantages](#key-advantages)\n* [Features](#features)\n * [Core Detection Features](#core-detection-features)\n * [Naive Bayes Classifier](#naive-bayes-classifier)\n * [Phishing Detection](#phishing-detection)\n * [Virus Scanning](#virus-scanning)\n * [Executable Detection](#executable-detection)\n * [NSFW Image Detection](#nsfw-image-detection)\n * [Toxicity Detection](#toxicity-detection)\n * [Macro Detection](#macro-detection)\n * [Language Detection](#language-detection)\n * [Pattern Recognition](#pattern-recognition)\n * [URL Analysis](#url-analysis)\n* [Comparison](#comparison)\n * [Spam Scanner vs. Alternatives](#spam-scanner-vs-alternatives)\n* [Architecture](#architecture)\n * [System Overview](#system-overview)\n * [Detection Flow](#detection-flow)\n * [Component Architecture](#component-architecture)\n* [Requirements](#requirements)\n * [System Requirements](#system-requirements)\n * [Dependencies](#dependencies)\n* [Quick Start](#quick-start)\n * [Basic Usage](#basic-usage)\n * [With Configuration](#with-configuration)\n * [Checking Specific Features](#checking-specific-features)\n* [API Documentation](#api-documentation)\n * [Constructor Options](#constructor-options)\n * [Methods](#methods)\n * [Result Object](#result-object)\n* [Advanced Usage](#advanced-usage)\n * [Custom Classifier](#custom-classifier)\n * [Custom Text Replacements](#custom-text-replacements)\n * [Language Filtering](#language-filtering)\n * [Performance Monitoring](#performance-monitoring)\n * [Selective Feature Disabling](#selective-feature-disabling)\n * [Custom Timeout](#custom-timeout)\n * [Custom Logger](#custom-logger)\n* [ARF (Abuse Reporting Format)](#arf-abuse-reporting-format)\n * [Parsing ARF Reports](#parsing-arf-reports)\n * [Creating ARF Reports](#creating-arf-reports)\n * [ARF Result Object](#arf-result-object)\n* [Mail Server Integration](#mail-server-integration)\n * [Postfix Integration](#postfix-integration)\n * [Dovecot Integration](#dovecot-integration)\n * [TCP Server Mode](#tcp-server-mode)\n* [Performance](#performance)\n * [Benchmarks](#benchmarks)\n * [Optimization Tips](#optimization-tips)\n * [Memory Usage](#memory-usage)\n* [Contributing](#contributing)\n * [Development Setup](#development-setup)\n * [Running Tests](#running-tests)\n* [License](#license)\n* [Support](#support)\n* [Acknowledgments](#acknowledgments)\n\n\n## Foreword\n\nSpam Scanner is a tool and service created after hitting countless roadblocks with existing spam-detection solutions. In other words, it's our current [plan for spam](https://forwardemail.net/blog/our-plan-for-spam) and our [better plan for spam](https://forwardemail.net/blog/a-better-plan-for-spam).\n\nOur goal is to build and utilize a scalable, performant, simple, easy to maintain, and powerful API for use in our service at [Forward Email](https://forwardemail.net) to limit spam and provide other measures to prevent attacks on our users.\n\nInitially we tried using [SpamAssassin](https://spamassassin.apache.org), and later evaluated [rspamd](https://rspamd.com) – but in the end we learned that all existing solutions (even ones besides these) are overtly complex, missing required features or documentation, incredibly challenging to configure; high-barrier to entry, or have proprietary storage backends (that could store and read your messages without your consent) that limit our scalability.\n\nTo us, we value privacy and the security of our data and users – specifically we have a \"Zero-Tolerance Policy\" on storing logs or metadata of any kind, whatsoever (see our [Privacy Policy](https://forwardemail.net/privacy-policy) for more on that). None of these solutions honored this privacy policy (without removing essential spam-detection functionality), so we had to create our own tool – thus \"Spam Scanner\" was born.\n\n---\n\n\n## Installation\n\n### npm (Recommended)\n\n```bash\nnpm install spamscanner\n```\n\nFor CLI usage:\n\n```bash\n# Install globally\nnpm install -g spamscanner\n\n# Or use npx without installing\nnpx spamscanner scan email.eml\n```\n\n### Standalone Binary (No Node.js Required)\n\nDownload a pre-built binary for your platform. These are self-contained executables that don't require Node.js.\n\nSee [CLI Installation](#cli-installation) for download links and platform-specific instructions.\n\n### ClamAV Installation (Optional)\n\n#### macOS\n\n```bash\nbrew install clamav\nfreshclam\n```\n\n#### Ubuntu/Debian\n\n```bash\nsudo apt-get update\nsudo apt-get install clamav clamav-daemon\nsudo freshclam\nsudo systemctl start clamav-daemon\n```\n\n#### CentOS/RHEL\n\n```bash\nsudo yum install clamav clamav-update\nsudo freshclam\n```\n\n> \\[!TIP]\n> See the [ClamAV configuration guide](https://github.com/spamscanner/spamscanner/blob/master/docs/clamav.md) for detailed installation instructions.\n\n---\n\n\n## CLI (Command Line Interface)\n\nSpamScanner provides a command-line interface for scanning emails directly from the terminal or integrating with mail servers.\n\n### CLI Installation\n\nSpamScanner can be installed via npm or as a standalone binary. The standalone binary includes Node.js and all dependencies, so no additional runtime is required.\n\n#### Install via npm (requires Node.js)\n\n```bash\n# Install globally\nnpm install -g spamscanner\n\n# Or use npx without installing\nnpx spamscanner --help\n```\n\n#### Install Standalone Binary\n\n##### macOS\n\n```bash\n# Download manually for Apple Silicon (M1/M2/M3)\ncurl -fsSL https://github.com/spamscanner/spamscanner/releases/latest/download/spamscanner-darwin-arm64 -o /usr/local/bin/spamscanner\nchmod +x /usr/local/bin/spamscanner\n```\n\n```bash\n# Download manually for Intel Mac\ncurl -fsSL https://github.com/spamscanner/spamscanner/releases/latest/download/spamscanner-darwin-x64 -o /usr/local/bin/spamscanner\nchmod +x /usr/local/bin/spamscanner\n```\n\n##### Linux\n\n```bash\n# Download and install to /usr/local/bin\nsudo curl -fsSL https://github.com/spamscanner/spamscanner/releases/latest/download/spamscanner-linux-x64 -o /usr/local/bin/spamscanner\nsudo chmod +x /usr/local/bin/spamscanner\n# Add to PATH if not already: export PATH=\"$HOME/.local/bin:$PATH\"\n```\n\n```bash\n# Or install to user directory (no sudo required)\nmkdir -p ~/.local/bin\ncurl -fsSL https://github.com/spamscanner/spamscanner/releases/latest/download/spamscanner-linux-x64 -o ~/.local/bin/spamscanner\nchmod +x ~/.local/bin/spamscanner\n# Add to PATH if not already: export PATH=\"$HOME/.local/bin:$PATH\"\n```\n\n##### Windows\n\n```powershell\n# Using PowerShell (run as Administrator)\nInvoke-WebRequest -Uri \"https://github.com/spamscanner/spamscanner/releases/latest/download/spamscanner-win-x64.exe\" -OutFile \"C:\\Program Files\\spamscanner\\spamscanner.exe\"\n# Add to PATH via System Properties > Environment Variables\n```\n\n```powershell\n# Or download to current directory\nInvoke-WebRequest -Uri \"https://github.com/spamscanner/spamscanner/releases/latest/download/spamscanner-win-x64.exe\" -OutFile \".\\spamscanner.exe\"\n# Add to PATH via System Properties > Environment Variables\n```\n\n##### Verify Installation\n\n```bash\n# Check version\nspamscanner version\n\n# Check for updates\nspamscanner update\n```\n\n#### Automatic Updates\n\nSpamScanner CLI automatically checks for updates once every 24 hours and displays a notification if a new version is available. You can also manually check for updates:\n\n```bash\n# Check for updates\nspamscanner update\n\n# Disable automatic update checks\nspamscanner scan email.eml --no-update-check\n```\n\nTo update to the latest version, simply re-run the installation command for your platform or use npm:\n\n```bash\n# Update via npm\nnpm update -g spamscanner\n\n# Or re-download the binary (macOS/Linux)\ncurl -fsSL https://github.com/spamscanner/spamscanner/releases/latest/download/spamscanner-$(uname -s | tr '[:upper:]' '[:lower:]')-$(uname -m | sed 's/x86_64/x64/' | sed 's/aarch64/arm64/') -o /usr/local/bin/spamscanner\nchmod +x /usr/local/bin/spamscanner\n```\n\n### Commands\n\n| Command | Description |\n| ------------------------- | --------------------- |\n| `spamscanner scan ` | Scan an email file |\n| `spamscanner scan -` | Scan email from stdin |\n| `spamscanner server` | Start TCP server mode |\n| `spamscanner help` | Show help message |\n| `spamscanner version` | Show version number |\n| `spamscanner update` | Check for updates |\n\n#### General Options\n\n| Option | Description |\n| ------------------- | --------------------------------------------- |\n| `-h, --help` | Show help |\n| `-v, --version` | Show version |\n| `-j, --json` | Output results as JSON |\n| `--verbose` | Show detailed output |\n| `--debug` | Enable debug mode |\n| `--timeout ` | Scan timeout in milliseconds (default: 30000) |\n| `--no-update-check` | Disable automatic update check |\n\n#### Spam Detection Options\n\nSpamScanner calculates a spam score based on multiple detection methods. You can configure which checks are included and customize the score weights.\n\n| Option | Description |\n| --------------------- | ------------------------------------------------- |\n| `--threshold ` | Spam score threshold (default: 5.0) |\n| `--check-classifier` | Include Bayesian classifier in scoring (default) |\n| `--check-phishing` | Include phishing detection in scoring (default) |\n| `--check-executables` | Include executable detection in scoring (default) |\n| `--check-macros` | Include macro detection in scoring (default) |\n| `--check-virus` | Include virus detection in scoring (default) |\n| `--check-nsfw` | Include NSFW detection in scoring (disabled) |\n| `--check-toxicity` | Include toxicity detection in scoring (disabled) |\n| `--no-classifier` | Disable Bayesian classifier scoring |\n| `--no-phishing` | Disable phishing scoring |\n| `--no-executables` | Disable executable scoring |\n| `--no-macros` | Disable macro scoring |\n| `--no-virus` | Disable virus scoring |\n\n#### Score Weights\n\nCustomize how much each detection type contributes to the total spam score:\n\n| Option | Description |\n| ------------------------ | ------------------------------------------- |\n| `--score-classifier ` | Classifier spam score weight (default: 5.0) |\n| `--score-phishing ` | Phishing score per issue (default: 5.0) |\n| `--score-executable ` | Executable score per file (default: 10.0) |\n| `--score-macro ` | Macro score per detection (default: 5.0) |\n| `--score-virus ` | Virus score per detection (default: 100.0) |\n| `--score-nsfw ` | NSFW score per detection (default: 3.0) |\n| `--score-toxicity ` | Toxicity score per detection (default: 3.0) |\n\n#### Header Options\n\nFor mail server integration, SpamScanner can add X-Spam headers to emails (similar to [SpamAssassin](https://github.com/apache/spamassassin) and [Stalwart](https://github.com/stalwartlabs/stalwart)):\n\n| Option | Description |\n| --------------------- | ------------------------------------------ |\n| `--add-headers` | Add X-Spam-* headers to output |\n| `--prepend-subject` | Prepend [SPAM] to subject if spam detected |\n| `--subject-tag ` | Custom subject tag (default: [SPAM]) |\n\n**X-Spam Headers Added:**\n\n| Header | Description |\n| --------------- | -------------------------------------------------------------------- |\n| `X-Spam-Status` | `Yes/No, score=X.X required=Y.Y tests=TEST1,TEST2,... version=X.X.X` |\n| `X-Spam-Score` | Numeric spam score (e.g., `7.5`) |\n| `X-Spam-Flag` | `YES` or `NO` |\n| `X-Spam-Tests` | Comma-separated list of triggered tests |\n\n#### Scanner Configuration Options\n\nThese options configure the underlying SpamScanner engine:\n\n| Option | Description |\n| -------------------------- | ------------------------------------------------------- |\n| `--languages ` | Comma-separated language codes (default: all languages) |\n| `--mixed-language` | Enable mixed language detection in emails |\n| `--no-macro-detection` | Disable macro detection in attachments |\n| `--no-pattern-recognition` | Disable advanced pattern recognition |\n| `--strict-idn` | Enable strict IDN/homograph detection |\n| `--nsfw-threshold ` | NSFW detection threshold 0.0-1.0 (default: 0.6) |\n| `--toxicity-threshold ` | Toxicity detection threshold 0.0-1.0 (default: 0.7) |\n| `--clamscan-path ` | Path to clamscan binary (default: /usr/bin/clamscan) |\n| `--clamdscan-path ` | Path to clamdscan binary (default: /usr/bin/clamdscan) |\n\n##### Supported Languages\n\nUse ISO 639-1 language codes with the `--languages` option. Pass an empty string or `all` to enable all languages (default).\n\n| Code | Language | Code | Language | Code | Language |\n| ---- | --------------- | ---- | ---------- | ---- | ---------- |\n| `en` | English | `fr` | French | `es` | Spanish |\n| `de` | German | `it` | Italian | `pt` | Portuguese |\n| `ru` | Russian | `ja` | Japanese | `ko` | Korean |\n| `zh` | Chinese | `ar` | Arabic | `hi` | Hindi |\n| `bn` | Bengali | `ur` | Urdu | `tr` | Turkish |\n| `pl` | Polish | `nl` | Dutch | `sv` | Swedish |\n| `no` | Norwegian | `da` | Danish | `fi` | Finnish |\n| `hu` | Hungarian | `cs` | Czech | `sk` | Slovak |\n| `sl` | Slovenian | `hr` | Croatian | `sr` | Serbian |\n| `bg` | Bulgarian | `ro` | Romanian | `el` | Greek |\n| `he` | Hebrew | `th` | Thai | `vi` | Vietnamese |\n| `id` | Indonesian | `ms` | Malay | `tl` | Tagalog |\n| `uk` | Ukrainian | `be` | Belarusian | `lt` | Lithuanian |\n| `lv` | Latvian | `et` | Estonian | `ca` | Catalan |\n| `eu` | Basque | `gl` | Galician | `ga` | Irish |\n| `gd` | Scottish Gaelic | `cy` | Welsh | `is` | Icelandic |\n| `mt` | Maltese | `af` | Afrikaans | `sw` | Swahili |\n| `am` | Amharic | `ha` | Hausa | `yo` | Yoruba |\n| `ig` | Igbo | `so` | Somali | `om` | Oromo |\n| `ti` | Tigrinya | `mg` | Malagasy | `ny` | Chichewa |\n| `sn` | Shona | `xh` | Xhosa | `zu` | Zulu |\n| `st` | Southern Sotho | `tn` | Tswana | | |\n\n#### Server Options\n\n| Option | Description |\n| --------------- | ------------------------------------ |\n| `--port ` | TCP server port (default: 7830) |\n| `--host ` | TCP server host (default: 127.0.0.1) |\n\n### Exit Codes\n\n| Code | Meaning |\n| ---- | ---------------- |\n| `0` | Clean (not spam) |\n| `1` | Spam detected |\n| `2` | Error occurred |\n\n### CLI Examples\n\n```bash\n# Scan a file\nspamscanner scan email.eml\n\n# Scan from stdin (for Postfix integration)\ncat email.eml | spamscanner scan -\n\n# Scan with JSON output (includes score and tests)\nspamscanner scan email.eml --json\n\n# Scan with verbose output\nspamscanner scan email.eml --verbose\n\n# Scan with custom spam threshold\nspamscanner scan email.eml --threshold 3.0\n\n# Scan with only classifier and phishing checks\nspamscanner scan email.eml --no-executables --no-macros --no-virus\n\n# Scan and add X-Spam headers (for mail server integration)\nspamscanner scan email.eml --add-headers\n\n# Scan, add headers, and prepend [SPAM] to subject\nspamscanner scan email.eml --add-headers --prepend-subject\n\n# Scan with custom subject tag\nspamscanner scan email.eml --add-headers --prepend-subject --subject-tag \"[JUNK]\"\n\n# Enable NSFW and toxicity checks with custom weights\nspamscanner scan email.eml --check-nsfw --check-toxicity --score-nsfw 5.0\n\n# Start TCP server on custom port\nspamscanner server --port 8080\n\n# Start TCP server with custom threshold\nspamscanner server --port 8080 --threshold 3.0\n\n# Scan with specific language support (English, Spanish, French only)\nspamscanner scan email.eml --languages en,es,fr\n\n# Scan with mixed language detection enabled\nspamscanner scan email.eml --mixed-language\n\n# Scan with strict IDN/homograph detection\nspamscanner scan email.eml --strict-idn\n\n# Scan with custom NSFW threshold (more sensitive)\nspamscanner scan email.eml --check-nsfw --nsfw-threshold 0.3\n\n# Scan with custom ClamAV paths\nspamscanner scan email.eml --clamscan-path /opt/clamav/bin/clamscan\n```\n\n#### Example JSON Output\n\n```json\n{\n \"isSpam\": true,\n \"score\": 7.5,\n \"threshold\": 5.0,\n \"tests\": [\"BAYES_SPAM(5.0)\", \"PHISHING_DETECTED(2.5)\"],\n \"message\": \"Spam\",\n \"results\": {\n \"classification\": { \"category\": \"spam\", \"probability\": 0.95 },\n \"phishing\": [{ \"type\": \"suspicious_link\", \"url\": \"http://example.com\" }]\n },\n \"headers\": {\n \"X-Spam-Status\": \"Yes, score=7.5 required=5.0 tests=BAYES_SPAM(5.0),PHISHING_DETECTED(2.5) version=6.0.1\",\n \"X-Spam-Score\": \"7.5\",\n \"X-Spam-Flag\": \"YES\",\n \"X-Spam-Tests\": \"BAYES_SPAM(5.0), PHISHING_DETECTED(2.5)\"\n }\n}\n```\n\n---\n\n\n## Why Spam Scanner\n\n> \\[!TIP]\n> Spam Scanner is the only modern, privacy-focused, Node.js-based spam detection solution with AI-powered features.\n\n### Key Advantages\n\n* **🔒 Privacy-First** - Zero logging, zero metadata storage\n* **🚀 Modern** - Built with Node.js 18+, ES modules, and latest AI models\n* **🎯 Accurate** - 88%+ detection accuracy with Naive Bayes classifier\n* **⚡ Fast** - Scans emails in under 3 seconds (with model caching)\n* **🛡️ Comprehensive** - 10+ detection methods (virus, phishing, NSFW, toxicity, macros, etc.)\n* **🌍 Multilingual** - Supports 40+ languages with automatic detection\n* **🔧 Easy to Use** - Simple API, extensive documentation, TypeScript support\n* **📊 Battle-Tested** - Used in production at Forward Email\n\n---\n\n\n## Features\n\nSpam Scanner includes modern, essential, and performant features that help reduce spam, phishing, and executable attacks.\n\n### Core Detection Features\n\n| Feature | Description | Status |\n| ----------------------------------------------------- | ------------------------------------------------------------------ | ------------ |\n| **[Naive Bayes Classifier](#naive-bayes-classifier)** | Machine learning spam classification trained on 100K+ emails | ✅ Production |\n| **[Phishing Detection](#phishing-detection)** | IDN homograph detection, confusables, suspicious link analysis | ✅ Production |\n| **[Virus Scanning](#virus-scanning)** | ClamAV integration for attachment scanning | ✅ Production |\n| **[Executable Detection](#executable-detection)** | Detects 195+ dangerous file extensions + magic number verification | ✅ Production |\n| **[NSFW Image Detection](#nsfw-image-detection)** | TensorFlow.js-powered image content analysis | ✅ Production |\n| **[Toxicity Detection](#toxicity-detection)** | AI-powered toxic language detection (threats, insults, harassment) | ✅ Production |\n| **[Macro Detection](#macro-detection)** | VBA, PowerShell, JavaScript, Batch script detection in attachments | ✅ Production |\n| **[Language Detection](#language-detection)** | Hybrid franc/lande detection for 40+ languages | ✅ Production |\n| **[Pattern Recognition](#pattern-recognition)** | Credit cards, phone numbers, IPs, Bitcoin addresses, etc. | ✅ Production |\n| **[URL Analysis](#url-analysis)** | TLD parsing, Cloudflare blocking detection, suspicious domains | ✅ Production |\n\n### Naive Bayes Classifier\n\nOur Naive Bayesian classifier is available in this [repository](classifier.json), the npm package, and is updated frequently as it gains upstream, anonymous, SHA-256 hashed data from [Forward Email](https://forwardemail.net).\n\n* **Training Data**: 100,000+ spam and ham emails\n* **Accuracy**: 88%+ classification accuracy\n* **Languages**: Supports 40+ languages with language-specific tokenization\n* **Stemming**: Porter Stemmer for English, Snowball for 15+ other languages\n* **Privacy**: All training data is anonymized and SHA-256 hashed\n\n### Phishing Detection\n\nAdvanced phishing detection using multiple techniques:\n\n* **IDN Homograph Detection**: Detects lookalike domains (e.g., `аpple.com` using Cyrillic \"а\")\n* **Confusables Integration**: Uses Unicode confusables database to detect character substitution\n* **TLD Analysis**: Validates TLDs and detects suspicious domain patterns\n* **Link Analysis**: Checks for mismatched display text and actual URLs\n* **Cloudflare Detection**: Identifies domains blocked by Cloudflare\n\n### Virus Scanning\n\nIntegrates with ClamAV for comprehensive virus detection:\n\n* **Real-time Scanning**: Scans all email attachments\n* **Buffer Support**: Direct buffer scanning without file I/O\n* **Timeout Protection**: Configurable scan timeouts\n* **Virus Database**: Uses ClamAV's regularly updated virus definitions\n\n### Executable Detection\n\nDetects dangerous executable files:\n\n* **195+ File Extensions**: exe, dll, bat, vbs, ps1, scr, pif, cmd, com, etc.\n* **Magic Number Verification**: Detects renamed executables by file content\n* **Office Macros**: Detects macro-enabled Office documents (docm, xlsm, pptm)\n* **Legacy Office**: Flags legacy Office formats (doc, xls, ppt) as high-risk\n* **PDF JavaScript**: Detects malicious JavaScript in PDF files\n* **Archive Detection**: Flags archives (zip, rar, 7z) that may hide executables\n\n### NSFW Image Detection\n\nAI-powered image content analysis using TensorFlow\\.js:\n\n* **Categories**: Porn, Hentai, Sexy, Neutral, Drawing\n* **Model**: NSFWJS model trained on 60K+ images\n* **Threshold**: Configurable detection threshold (default: 0.7)\n* **Performance**: Model caching for fast subsequent scans\n* **Formats**: Supports JPEG, PNG, GIF, WebP, BMP\n\n### Toxicity Detection\n\nDetects toxic language using TensorFlow\\.js Toxicity model:\n\n* **Categories**: Identity attack, insult, obscenity, severe toxicity, sexual explicit, threat\n* **Threshold**: Configurable toxicity threshold (default: 0.7)\n* **Languages**: Optimized for English, supports other languages\n* **Performance**: Model caching for fast subsequent scans\n\n### Macro Detection\n\nDetects malicious macros in email content and attachments:\n\n* **VBA Macros**: Detects Visual Basic for Applications code\n* **PowerShell**: Detects PowerShell scripts and commands\n* **JavaScript**: Detects JavaScript code in emails\n* **Batch Scripts**: Detects Windows batch files\n* **Office Documents**: Scans docm, xlsm, pptm, xlam, dotm, xltm, potm\n* **PDF JavaScript**: Detects JavaScript in PDF attachments\n\n### Language Detection\n\nHybrid language detection using franc and lande:\n\n* **40+ Languages**: Supports all major world languages\n* **Automatic Detection**: Detects language from email content\n* **Fallback System**: Uses lande when franc returns \"undetermined\"\n* **Mixed Language Support**: Optional mixed language detection\n* **Language Filtering**: Filter results to supported languages only\n\n### Pattern Recognition\n\nDetects various patterns in email content:\n\n* **Credit Cards**: Visa, MasterCard, Amex, Discover, etc.\n* **Phone Numbers**: International phone number formats\n* **Email Addresses**: RFC-compliant email detection\n* **IP Addresses**: IPv4 and IPv6 addresses\n* **URLs**: Full URL extraction and analysis\n* **Bitcoin Addresses**: Cryptocurrency wallet addresses\n* **MAC Addresses**: Network hardware addresses\n* **Hex Colors**: Color codes (#RRGGBB)\n* **Floating Point Numbers**: Decimal numbers\n* **Dates**: Multiple date formats (MM/DD/YYYY, YYYY-MM-DD, etc.)\n* **File Paths**: Windows and Unix file paths\n\n### URL Analysis\n\nComprehensive URL analysis and validation:\n\n* **TLD Parsing**: Uses tldts for accurate TLD extraction\n* **Domain Analysis**: Extracts domain, subdomain, public suffix\n* **IP Detection**: Identifies IP-based URLs\n* **Cloudflare Check**: Detects Cloudflare-blocked domains\n* **URL Normalization**: Normalizes URLs for consistent analysis\n* **Suspicious Pattern Detection**: Identifies phishing URL patterns\n\n---\n\n\n## Comparison\n\n### Spam Scanner vs. Alternatives\n\n| Feature | Spam Scanner | SpamAssassin | rspamd | ClamAV |\n| ----------------------------- | :----------: | :-----------: | :-----------: | :-----: |\n| **License** | BSL 1.1 | Apache 2.0 | Apache 2.0 | GPLv2 |\n| **Language** | Node.js | Perl | C | C |\n| **Modern Architecture** | Yes | No | Partial | No |\n| **Easy to Use** | Yes | No | No | Yes |\n| **Privacy-Focused** | Yes | Partial | Partial | Yes |\n| **Naive Bayes Classifier** | Yes | Yes | Yes | No |\n| **Virus Scanning** | Yes | Yes | Yes | Yes |\n| **Phishing Detection** | Yes | Yes | Yes | No |\n| **IDN Homograph Detection** | Yes | No | Yes | No |\n| **NSFW Image Detection** | Yes | No | No | No |\n| **Toxicity Detection** | Yes | No | No | No |\n| **Macro Detection** | Yes | Yes | Yes | Yes |\n| **Language Detection** | Yes (40+) | Yes (limited) | Yes (limited) | No |\n| **Pattern Recognition** | Yes | Yes | Yes | No |\n| **Executable Detection** | Yes (195+) | Yes | Yes | Yes |\n| **Magic Number Verification** | Yes | No | No | Yes |\n| **PDF JavaScript Detection** | Yes | No | No | Partial |\n| **Archive Detection** | Yes | Yes | Yes | Yes |\n| **Performance Metrics** | Yes | No | Yes | No |\n| **TypeScript Support** | Yes | No | No | No |\n| **Active Development** | Yes | Yes | Yes | Yes |\n| **Production Ready** | Yes | Yes | Yes | Yes |\n\n> \\[!NOTE]\n> **Alternative to SpamAssassin**: Spam Scanner provides a modern, Node.js-based alternative to SpamAssassin with AI-powered features and better privacy.\n>\n> **Alternative to rspamd**: Spam Scanner offers easier configuration and better documentation than rspamd, with comparable detection accuracy.\n>\n> **Alternative to ClamAV**: While Spam Scanner uses ClamAV for virus scanning, it provides comprehensive spam and phishing detection that ClamAV doesn't offer.\n\n---\n\n\n## Architecture\n\n### System Overview\n\n```mermaid\ngraph TB\n A[Email Input] --> B{Spam Scanner}\n B --> C[Preprocessing]\n C --> D[Language Detection]\n D --> E[Tokenization]\n E --> F[Naive Bayes Classification]\n\n B --> G[Phishing Detection]\n G --> G1[IDN Homograph Check]\n G --> G2[Confusables Analysis]\n G --> G3[URL Analysis]\n\n B --> H[Attachment Scanning]\n H --> H1[Virus Scan]\n H --> H2[Executable Check]\n H --> H3[Macro Detection]\n H --> H4[NSFW Detection]\n\n B --> I[Content Analysis]\n I --> I1[Toxicity Detection]\n I --> I2[Pattern Recognition]\n\n F --> J[Result Aggregation]\n G --> J\n H --> J\n I --> J\n\n J --> K{Is Spam?}\n K -->|Yes| L[Spam Result]\n K -->|No| M[Ham Result]\n```\n\n### Detection Flow\n\n```mermaid\nsequenceDiagram\n participant Client\n participant Scanner\n participant Classifier\n participant ClamAV\n participant TensorFlow\n\n Client->>Scanner: scan(email)\n Scanner->>Scanner: Parse Email\n Scanner->>Scanner: Extract URLs\n Scanner->>Scanner: Detect Language\n\n par Parallel Detection\n Scanner->>Classifier: Classify Tokens\n Scanner->>ClamAV: Scan Attachments\n Scanner->>TensorFlow: Detect NSFW\n Scanner->>TensorFlow: Detect Toxicity\n Scanner->>Scanner: Check Phishing\n Scanner->>Scanner: Check Macros\n end\n\n Scanner->>Scanner: Aggregate Results\n Scanner->>Client: Return Result\n```\n\n### Component Architecture\n\n```mermaid\ngraph LR\n A[Spam Scanner] --> B[Core Engine]\n A --> C[Classifiers]\n A --> D[Detectors]\n A --> E[Analyzers]\n\n B --> B1[Email Parser]\n B --> B2[Tokenizer]\n B --> B3[Preprocessor]\n\n C --> C1[Naive Bayes]\n C --> C2[TensorFlow NSFW]\n C --> C3[TensorFlow Toxicity]\n\n D --> D1[Phishing Detector]\n D --> D2[Virus Scanner]\n D --> D3[Macro Detector]\n D --> D4[Executable Detector]\n\n E --> E1[Language Analyzer]\n E --> E2[URL Analyzer]\n E --> E3[Pattern Analyzer]\n```\n\n---\n\n\n## Requirements\n\n> \\[!WARNING]\n> ClamAV is required for virus scanning. If you do not have it installed, virus scanning will be disabled.\n\n### System Requirements\n\n* **Node.js**: >= 18.0.0\n* **ClamAV**: Latest version (for virus scanning)\n* **Memory**: 2GB+ RAM recommended (for TensorFlow models)\n* **Disk Space**: 500MB+ (for models and virus definitions)\n\n### Dependencies\n\n* **@tensorflow/tfjs-node**: For NSFW and toxicity detection\n* **@ladjs/naivebayes**: For spam classification\n* **clamscan**: For virus scanning\n* **mailparser**: For email parsing\n* **natural**: For NLP and tokenization\n* **tldts**: For TLD parsing\n* **confusables**: For Unicode confusables detection\n\n---\n\n\n## Quick Start\n\n### Basic Usage\n\n```js\nimport SpamScanner from 'spamscanner';\n\nconst scanner = new SpamScanner();\n\n// Raw email string or Buffer\nconst email = `\nFrom: sender@example.com\nTo: recipient@example.com\nSubject: Test Email\n\nThis is a test email.\n`;\n\nconst result = await scanner.scan(email);\n\nconsole.log(result);\n// {\n// isSpam: false,\n// message: 'Ham',\n// results: { ... },\n// ...\n// }\n```\n\n### With Configuration\n\n```js\nimport SpamScanner from 'spamscanner';\n\nconst scanner = new SpamScanner({\n // Enable performance metrics\n enablePerformanceMetrics: true,\n\n // Filter to supported languages\n supportedLanguages: ['en', 'es', 'fr', 'de'],\n\n // Enable macro detection\n enableMacroDetection: true,\n\n // Set scan timeout\n timeout: 30000,\n\n // Custom ClamAV configuration\n clamscan: {\n preference: 'clamdscan',\n clamdscanPath: '/usr/bin/clamdscan',\n },\n});\n\nconst result = await scanner.scan(email);\n```\n\n### Checking Specific Features\n\n```js\n// Check if email is spam\nif (result.isSpam) {\n console.log('Spam detected!');\n console.log('Reason:', result.message);\n}\n\n// Check for viruses\nif (result.results.viruses && result.results.viruses.length > 0) {\n console.log('Viruses found:', result.results.viruses);\n}\n\n// Check for phishing\nif (result.results.phishing && result.results.phishing.length > 0) {\n console.log('Phishing detected:', result.results.phishing);\n}\n\n// Check for executables\nif (result.results.executables && result.results.executables.length > 0) {\n console.log('Executables found:', result.results.executables);\n}\n\n// Check for NSFW content\nif (result.results.nsfw && result.results.nsfw.length > 0) {\n console.log('NSFW content detected:', result.results.nsfw);\n}\n\n// Check for toxic language\nif (result.results.toxicity && result.results.toxicity.length > 0) {\n console.log('Toxic language detected:', result.results.toxicity);\n}\n```\n\n---\n\n\n## API Documentation\n\n### Constructor Options\n\n#### `new SpamScanner(options)`\n\nCreates a new Spam Scanner instance.\n\n##### Options\n\n| Option | Type | Default | Description |\n| ---------------------------------- | ------------- | --------- | ----------------------------------------------------------------------------- |\n| `enableMacroDetection` | `boolean` | `true` | Enable macro detection in emails and attachments |\n| `enablePerformanceMetrics` | `boolean` | `false` | Track and return performance metrics |\n| `timeout` | `number` | `30000` | Timeout in milliseconds for scans (virus, URL checks) |\n| `supportedLanguages` | `string[]` | `['en']` | Array of supported language codes. Empty array `[]` = all languages supported |\n| `enableMixedLanguageDetection` | `boolean` | `false` | Enable detection of mixed languages in emails |\n| `enableAdvancedPatternRecognition` | `boolean` | `true` | Enable advanced pattern recognition (credit cards, phones, etc.) |\n| `toxicityThreshold` | `number` | `0.7` | Threshold for toxicity detection (0.0-1.0, higher = more strict) |\n| `nsfwThreshold` | `number` | `0.6` | Threshold for NSFW detection (0.0-1.0, higher = more strict) |\n| `debug` | `boolean` | `false` | Enable debug logging |\n| `logger` | `object` | `console` | Custom logger object (must have `log`, `error`, `warn` methods) |\n| `clamscan` | `object` | See below | ClamAV configuration options |\n| `classifier` | `object` | `null` | Custom Naive Bayes classifier data |\n| `replacements` | `Map\\|object` | `null` | Custom text replacements for preprocessing |\n\n##### ClamAV Options (`clamscan`)\n\n| Option | Type | Default | Description |\n| -------------------- | -------------- | ---------------------- | ------------------------------------------------ |\n| `removeInfected` | `boolean` | `false` | Remove infected files |\n| `quarantineInfected` | `boolean` | `false` | Quarantine infected files |\n| `scanLog` | `string\\|null` | `null` | Path to scan log file |\n| `debugMode` | `boolean` | `false` | Enable ClamAV debug mode |\n| `fileList` | `string\\|null` | `null` | Path to file list |\n| `scanRecursively` | `boolean` | `true` | Scan directories recursively |\n| `clamscanPath` | `string` | `'/usr/bin/clamscan'` | Path to clamscan binary |\n| `clamdscanPath` | `string` | `'/usr/bin/clamdscan'` | Path to clamdscan binary |\n| `preference` | `string` | `'clamdscan'` | Preferred scanner: `'clamdscan'` or `'clamscan'` |\n\n##### Example\n\n```js\nconst scanner = new SpamScanner({\n enableMacroDetection: true,\n enablePerformanceMetrics: true,\n timeout: 60000,\n supportedLanguages: ['en', 'es', 'fr', 'de', 'ja', 'zh'],\n enableMixedLanguageDetection: false,\n enableAdvancedPatternRecognition: true,\n debug: false,\n logger: console,\n clamscan: {\n preference: 'clamdscan',\n clamdscanPath: '/usr/bin/clamdscan',\n scanRecursively: true,\n debugMode: false,\n },\n});\n```\n\n---\n\n### Methods\n\n#### `scanner.scan(source)`\n\nScans an email for spam, viruses, phishing, and other threats.\n\n##### Parameters\n\n* `source` (`string` | `Buffer`) - Raw email content (RFC 822 format)\n\n##### Returns\n\n`Promise` - Scan result object (see [Result Object](#result-object))\n\n##### Example\n\n```js\nconst result = await scanner.scan(emailString);\n```\n\n##### Edge Cases\n\n* **Empty email**: Returns `isSpam: false` with empty results\n* **Invalid email format**: Attempts to parse, may return partial results\n* **Timeout**: Returns partial results if scan exceeds `timeout` option\n* **ClamAV unavailable**: Skips virus scanning, continues with other checks\n* **TensorFlow model loading**: First scan may take 30+ seconds, subsequent scans are fast (models cached)\n\n---\n\n#### `scanner.getTokensAndMailFromSource(source)`\n\nParses email and extracts tokens for classification.\n\n##### Parameters\n\n* `source` (`string` | `Buffer`) - Raw email content\n\n##### Returns\n\n`Promise` - Object with `tokens` (array) and `mail` (parsed email object)\n\n##### Example\n\n```js\nconst {tokens, mail} = await scanner.getTokensAndMailFromSource(emailString);\nconsole.log('Tokens:', tokens);\nconsole.log('Subject:', mail.subject);\n```\n\n---\n\n#### `scanner.getClassification(tokens)`\n\nClassifies tokens as spam or ham using Naive Bayes classifier.\n\n##### Parameters\n\n* `tokens` (`string[]`) - Array of tokens from email\n\n##### Returns\n\n`Promise` - Classification result with `category` and `probability`\n\n##### Example\n\n```js\nconst classification = await scanner.getClassification(tokens);\nconsole.log('Category:', classification.category); // 'spam' or 'ham'\nconsole.log('Probability:', classification.probability); // 0.0 - 1.0\n```\n\n---\n\n#### `scanner.getPhishingResults(mail)`\n\nDetects phishing attempts in email.\n\n##### Parameters\n\n* `mail` (`object`) - Parsed email object from `mailparser`\n\n##### Returns\n\n`Promise` - Array of phishing detection results\n\n##### Example\n\n```js\nconst phishing = await scanner.getPhishingResults(mail);\n// [\n// {\n// type: 'idn_homograph',\n// domain: 'аpple.com',\n// message: 'IDN homograph attack detected'\n// }\n// ]\n```\n\n---\n\n#### `scanner.getExecutableResults(mail)`\n\nDetects executable files in email attachments.\n\n##### Parameters\n\n* `mail` (`object`) - Parsed email object from `mailparser`\n\n##### Returns\n\n`Promise` - Array of executable detection results\n\n##### Example\n\n```js\nconst executables = await scanner.getExecutableResults(mail);\n// [\n// {\n// filename: 'malware.exe',\n// type: 'executable',\n// extension: 'exe',\n// risk: 'high'\n// }\n// ]\n```\n\n---\n\n#### `scanner.getVirusResults(mail)`\n\nScans email attachments for viruses using ClamAV.\n\n##### Parameters\n\n* `mail` (`object`) - Parsed email object from `mailparser`\n\n##### Returns\n\n`Promise` - Array of virus detection results\n\n##### Example\n\n```js\nconst viruses = await scanner.getVirusResults(mail);\n// [\n// {\n// filename: 'infected.pdf',\n// virus: ['Trojan.PDF.Generic'],\n// type: 'virus'\n// }\n// ]\n```\n\n---\n\n#### `scanner.getMacroResults(mail)`\n\nDetects macros in email content and attachments.\n\n##### Parameters\n\n* `mail` (`object`) - Parsed email object from `mailparser`\n\n##### Returns\n\n`Promise` - Array of macro detection results\n\n##### Example\n\n```js\nconst macros = await scanner.getMacroResults(mail);\n// [\n// {\n// type: 'vba_macro',\n// message: 'VBA macro detected in email content'\n// }\n// ]\n```\n\n---\n\n#### `scanner.getNSFWResults(mail)`\n\nDetects NSFW content in image attachments using TensorFlow\\.js.\n\n##### Parameters\n\n* `mail` (`object`) - Parsed email object from `mailparser`\n\n##### Returns\n\n`Promise` - Array of NSFW detection results\n\n##### Example\n\n```js\nconst nsfw = await scanner.getNSFWResults(mail);\n// [\n// {\n// type: 'nsfw',\n// filename: 'image.jpg',\n// category: 'Porn',\n// probability: 0.85,\n// description: 'NSFW image detected: Porn (85.0%)'\n// }\n// ]\n```\n\n---\n\n#### `scanner.getToxicityResults(mail)`\n\nDetects toxic language in email content using TensorFlow\\.js.\n\n##### Parameters\n\n* `mail` (`object`) - Parsed email object from `mailparser`\n\n##### Returns\n\n`Promise` - Array of toxicity detection results\n\n##### Example\n\n```js\nconst toxicity = await scanner.getToxicityResults(mail);\n// [\n// {\n// type: 'toxicity',\n// category: 'threat',\n// probability: 0.92,\n// description: 'Toxic content detected: threat (92.0%)'\n// },\n// {\n// type: 'toxicity',\n// category: 'insult',\n// probability: 0.78,\n// description: 'Toxic content detected: insult (78.0%)'\n// }\n// ]\n```\n\n---\n\n#### `scanner.getTokens(str, locale, isHTML)`\n\nTokenizes text for classification.\n\n##### Parameters\n\n* `str` (`string`) - Text to tokenize\n* `locale` (`string`) - Language code (e.g., \"en\", \"es\", \"fr\")\n* `isHTML` (`boolean`) - Whether text contains HTML (default: `false`)\n\n##### Returns\n\n`Promise` - Array of tokens\n\n##### Example\n\n```js\nconst tokens = await scanner.getTokens('Hello world', 'en', false);\n// ['hello', 'world']\n```\n\n---\n\n#### `scanner.parseLocale(locale)`\n\nNormalizes language codes to standard format.\n\n##### Parameters\n\n* `locale` (`string`) - Language code or locale string\n\n##### Returns\n\n`string` - Normalized language code\n\n##### Example\n\n```js\nconst normalized = scanner.parseLocale('en-US');\n// 'en'\n```\n\n---\n\n#### `scanner.detectLanguageHybrid(text)`\n\nDetects language using hybrid franc/lande approach.\n\n##### Parameters\n\n* `text` (`string`) - Text to analyze\n\n##### Returns\n\n`Promise` - Detected language code\n\n##### Example\n\n```js\nconst language = await scanner.detectLanguageHybrid('Bonjour le monde');\n// 'fr'\n```\n\n---\n\n#### `scanner.extractAllUrls(mail, originalSource)`\n\nExtracts all URLs from email.\n\n##### Parameters\n\n* `mail` (`object`) - Parsed email object\n* `originalSource` (`string`) - Original email source\n\n##### Returns\n\n`string[]` - Array of URLs\n\n##### Example\n\n```js\nconst urls = scanner.extractAllUrls(mail, emailString);\n// ['https://example.com', 'http://test.com']\n```\n\n---\n\n#### `scanner.parseUrlWithTldts(url)`\n\nParses URL using tldts for accurate TLD extraction.\n\n##### Parameters\n\n* `url` (`string`) - URL to parse\n\n##### Returns\n\n`object` - Parsed URL components\n\n##### Example\n\n```js\nconst parsed = scanner.parseUrlWithTldts('https://subdomain.example.co.uk/path');\n// {\n// domain: 'example.co.uk',\n// subdomain: 'subdomain',\n// hostname: 'subdomain.example.co.uk',\n// publicSuffix: 'co.uk',\n// isIp: false\n// }\n```\n\n---\n\n### Result Object\n\nThe `scan()` method returns a comprehensive result object:\n\n```js\n{\n // Overall spam classification\n isSpam: boolean,\n message: string, // 'Ham' or 'Spam: '\n\n // Detection results\n results: {\n // Classification details\n classification: {\n category: 'spam' | 'ham',\n probability: number\n },\n\n // Phishing detection\n phishing: [\n {\n type: 'idn_homograph' | 'suspicious_link' | 'confusables',\n domain: string,\n message: string\n }\n ],\n\n // Executable detection\n executables: [\n {\n filename: string,\n type: 'executable' | 'office_document' | 'legacy_office' | 'pdf_javascript' | 'archive',\n extension: string,\n risk: 'high' | 'medium' | 'low'\n }\n ],\n\n // Macro detection\n macros: [\n {\n type: 'vba_macro' | 'powershell' | 'javascript' | 'batch',\n message: string\n }\n ],\n\n // Arbitrary results (custom detections)\n arbitrary: [],\n\n // Virus scanning\n viruses: [\n {\n filename: string,\n virus: string[],\n type: 'virus'\n }\n ],\n\n // Pattern recognition\n patterns: {\n credit_cards: number,\n phone_numbers: number,\n emails: number,\n ips: number,\n urls: number,\n bitcoin: number,\n dates: number,\n file_paths: number\n },\n\n // IDN homograph attack detection\n idnHomographAttack: [],\n\n // Toxicity detection (array of results)\n toxicity: [\n {\n type: 'toxicity',\n category: 'identity_attack' | 'insult' | 'obscene' | 'severe_toxicity' | 'sexual_explicit' | 'threat',\n probability: number,\n description: string\n }\n ],\n\n // NSFW detection (array of results)\n nsfw: [\n {\n type: 'nsfw',\n filename: string,\n category: 'Porn' | 'Hentai' | 'Sexy' | 'Neutral' | 'Drawing',\n probability: number,\n description: string\n }\n ]\n },\n\n // All URLs extracted from email\n links: string[],\n\n // Tokens extracted from email\n tokens: string[],\n\n // Email metadata\n mail: {\n from: object,\n to: object[],\n subject: string,\n text: string,\n html: string,\n attachments: object[],\n headers: object\n },\n\n // Performance metrics (if enabled)\n metrics: {\n totalTime: number, // milliseconds\n classificationTime: number,\n phishingTime: number,\n executableTime: number,\n macroTime: number,\n virusTime: number,\n patternTime: number,\n idnTime: number,\n memoryUsage: object\n }\n}\n```\n\n---\n\n\n## Advanced Usage\n\n### Custom Classifier\n\n```js\nimport SpamScanner from 'spamscanner';\nimport NaiveBayes from '@ladjs/naivebayes';\n\n// Train custom classifier\nconst classifier = new NaiveBayes();\nclassifier.learn('buy viagra now', 'spam');\nclassifier.learn('hello friend', 'ham');\n\nconst scanner = new SpamScanner({\n classifier: classifier.toJson()\n});\n```\n\n### Custom Text Replacements\n\n```js\nconst scanner = new SpamScanner({\n replacements: new Map([\n ['u', 'you'],\n ['ur', 'your'],\n ['r', 'are'],\n ['b4', 'before']\n ])\n});\n```\n\n### Language Filtering\n\n```js\n// Only accept English, Spanish, and French emails\nconst scanner = new SpamScanner({\n supportedLanguages: ['en', 'es', 'fr']\n});\n\n// Accept all languages\nconst scanner2 = new SpamScanner({\n supportedLanguages: []\n});\n```\n\n### Performance Monitoring\n\n```js\nconst scanner = new SpamScanner({\n enablePerformanceMetrics: true\n});\n\nconst result = await scanner.scan(email);\n\nconsole.log('Total scan time:', result.metrics.totalTime, 'ms');\nconsole.log('Classification time:', result.metrics.classificationTime, 'ms');\nconsole.log('Virus scan time:', result.metrics.virusScanTime, 'ms');\n```\n\n### Selective Feature Disabling\n\n```js\n// Disable macro detection for performance\nconst scanner = new SpamScanner({\n enableMacroDetection: false\n});\n\n// Disable advanced pattern recognition\nconst scanner2 = new SpamScanner({\n enableAdvancedPatternRecognition: false\n});\n```\n\n### Custom Timeout\n\n```js\n// Set 60-second timeout for slow scans\nconst scanner = new SpamScanner({\n timeout: 60000\n});\n```\n\n### Custom Logger\n\n```js\nimport winston from 'winston';\n\nconst logger = winston.createLogger({\n level: 'info',\n format: winston.format.json(),\n transports: [\n new winston.transports.File({ filename: 'spam-scanner.log' })\n ]\n});\n\nconst scanner = new SpamScanner({\n debug: true,\n logger: logger\n});\n```\n\n---\n\n\n## ARF (Abuse Reporting Format)\n\nSpamScanner includes a native [ARF (Abuse Reporting Format)](https://www.rfc-editor.org/rfc/rfc5965.html) parser for handling email feedback reports from ISPs and email providers.\n\n### Parsing ARF Reports\n\n```js\nimport {ArfParser} from 'spamscanner/arf';\nimport fs from 'node:fs';\n\n// Read an ARF report email\nconst arfEmail = fs.readFileSync('feedback_report.eml');\n\n// Parse the ARF report\nconst report = await ArfParser.parse(arfEmail);\n\nconsole.log('Feedback Type:', report.feedbackType);\nconsole.log('Source IP:', report.sourceIp);\nconsole.log('Original Sender:', report.originalMailFrom);\nconsole.log('Original Recipients:', report.originalRcptTo);\n```\n\n#### Safe Parsing\n\n```js\n// Use tryParse for safe parsing (returns null if not ARF)\nconst report = await ArfParser.tryParse(emailContent);\n\nif (report) {\n console.log('ARF report detected:', report.feedbackType);\n} else {\n console.log('Not an ARF report');\n}\n```\n\n### Creating ARF Reports\n\n```js\nimport {ArfParser} from 'spamscanner/arf';\n\nconst arfMessage = ArfParser.create({\n feedbackType: 'abuse',\n userAgent: 'MyMailServer/1.0',\n from: 'abuse@yourdomain.com',\n to: 'abuse@theirisp.com',\n originalMessage: originalEmailContent,\n humanReadable: 'This email was reported as spam by our users.',\n sourceIp: '192.168.1.100',\n originalMailFrom: 'spammer@example.com',\n originalRcptTo: ['victim@yourdomain.com'],\n arrivalDate: new Date(),\n reportingMta: 'mail.yourdomain.com',\n});\n\n// Send arfMessage to the abuse address\n```\n\n### ARF Result Object\n\n| Property | Type | Description |\n| ------------------ | ---------- | ----------------------------------------------------------------------------- |\n| `isArf` | `boolean` | Whether this is a valid ARF message |\n| `feedbackType` | `string` | Type: `abuse`, `fraud`, `virus`, `other`, `not-spam`, `auth-failure`, `dmarc` |\n| `userAgent` | `string` | User agent that generated the report |\n| `version` | `string` | ARF version (usually \"1\") |\n| `sourceIp` | `string` | Source IP of the original message |\n| `originalMailFrom` | `string` | Original MAIL FROM address |\n| `originalRcptTo` | `string[]` | Original RCPT TO addresses |\n| `arrivalDate` | `Date` | When the original message arrived |\n| `reportingMta` | `object` | Reporting MTA info (`{type, name}`) |\n| `incidents` | `number` | Number of incidents reported |\n| `humanReadable` | `string` | Human-readable description |\n| `originalMessage` | `string` | The original reported message |\n| `originalHeaders` | `object` | Parsed headers from original message |\n\n---\n\n\n## Mail Server Integration\n\nSpamScanner can be integrated with popular mail servers like [Postfix](https://www.postfix.org/) and [Dovecot](https://www.dovecot.org/) as a content filter.\n\n### Postfix Integration\n\n#### Using Pipe Content Filter\n\nThis is the recommended method for most Postfix setups.\n\n1. **Create a dedicated user** (recommended for security):\n\n```bash\nsudo useradd -r -s /bin/false spamscanner\n```\n\n2. **Edit `/etc/postfix/master.cf`**:\n\n```cf\n# SpamScanner content filter\nspamscanner unix - n n - - pipe\n flags=Rq user=spamscanner argv=/usr/local/bin/spamscanner scan -\n```\n\n3. **Edit `/etc/postfix/main.cf`**:\n\n```cf\ncontent_filter = spamscanner:dummy\n```\n\n4. **Reload Postfix**:\n\n```bash\nsudo postfix reload\n```\n\nPostfix will now pipe all incoming emails to SpamScanner. If SpamScanner exits with code 1 (spam), Postfix will reject the message.\n\n### Dovecot Integration\n\n#### Using Sieve and Pipe\n\n1. **Enable Pigeonhole Sieve** (ensure `dovecot-pigeonhole` is installed)\n\n2. **Create a Sieve script** (`/var/lib/dovecot/sieve/default.sieve`):\n\n```sieve\nrequire [\"vnd.dovecot.pipe\"];\n\n# Scan all incoming mail\nif header :contains \"X-Spam-Flag\" \"NO\" {\n pipe \"/usr/local/bin/spamscanner-sieve-helper\";\n}\n```\n\n3. **Create a helper script** (`/usr/local/bin/spamscanner-sieve-helper`):\n\n```bash\n#!/bin/bash\nEMAIL=$(cat)\nRESULT=$(echo \"$EMAIL\" | /usr/local/bin/spamscanner scan -)\nif [[ $? -eq 1 ]]; then\n echo \"X-Spam-Flag: YES\" | cat - <(echo \"$EMAIL\")\nelse\n echo \"$EMAIL\"\nfi\n```\n\n### TCP Server Mode\n\nFor high-volume environments, run SpamScanner as a persistent TCP server:\n\n```bash\n# Start the server\nspamscanner server --port 7830 --host 127.0.0.1\n```\n\n#### Systemd Service\n\nCreate `/etc/systemd/system/spamscanner.service`:\n\n```ini\n[Unit]\nDescription=SpamScanner TCP Server\nAfter=network.target\n\n[Service]\nType=simple\nUser=spamscanner\nExecStart=/usr/local/bin/spamscanner server --port 7830\nRestart=always\nRestartSec=5\n\n[Install]\nWantedBy=multi-user.target\n```\n\nEnable and start:\n\n```bash\nsudo systemctl enable spamscanner\nsudo systemctl start spamscanner\n```\n\n#### Client Example\n\n```bash\n# Send email to TCP server and get JSON response\ncat email.eml | nc localhost 7830\n```\n\n---\n\n\n## Performance\n\n### Benchmarks\n\n| Scan Type | First Scan | Subsequent Scans | Notes |\n| --------------------------- | ------------ | ---------------- | ------------------------ |\n| **Small Email** (< 10KB) | 2-3s | 200-500ms | No attachments |\n| **Medium Email** (10-100KB) | 3-5s | 500ms-1s | 1-2 attachments |\n| **Large Email** (100KB-1MB) | 5-10s | 1-3s | Multiple attachments |\n| **With NSFW Detection** | +30s (first) | +100-200ms | TensorFlow model loading |\n| **With Toxicity Detection** | +30s (first) | +100-200ms | TensorFlow model loading |\n\n> \\[!NOTE]\n> First scans with TensorFlow models (NSFW/toxicity) take 30+ seconds due to model loading. Subsequent scans are fast because models are cached in memory.\n\n### Optimization Tips\n\n1. **Model Caching**: Keep scanner instance alive to cache TensorFlow models\n2. **Disable Unused Features**: Turn off macro detection or pattern recognition if not needed\n3. **Adjust Timeout**: Increase timeout for large emails with many attachments\n4. **Use clamdscan**: Prefer `clamdscan` over `clamscan` for faster virus scanning\n5. **Limit Languages**: Specify `supportedLanguages` to skip unnecessary language detection\n\n### Memory Usage\n\n* **Base**: 50-100MB\n* **With TensorFlow Models**: 500MB-1GB\n* **Per Scan**: 10-50MB (temporary)\n\n---\n\n\n## Contributing\n\nWe welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.\n\n### Development Setup\n\n```bash\n# Clone repository\ngit clone https://github.com/spamscanner/spamscanner.git\ncd spamscanner\n\n# Install dependencies\npnpm install\n\n# Run tests\nnpm test\n\n# Run tests with coverage\nnpm run test-coverage\n\n# Build\npnpm run build\n```\n\n### Running Tests\n\n```bash\n# All tests\nnpm test\n\n# Specific test file\nnode --test test/test.js\n\n# With coverage\nnpm run test-coverage\n```\n\n---\n\n\n## License\n\n[Business Source License 1.1](LICENSE) © [Forward Email](https://forwardemail.net)\n\n\n## Support\n\n* **Documentation**: \n* **Issues**: [GitHub Issues](https://github.com/spamscanner/spamscanner/issues)\n* **Email**: \n\n---\n\n\n## Acknowledgments\n\n* [Forward Email](https://forwardemail.net) - Production usage and testing\n* [TensorFlow.js](https://www.tensorflow.org/js) - NSFW and toxicity detection\n* [ClamAV](https://www.clamav.net/) - Virus scanning\n* [Natural](https://github.com/NaturalNode/natural) - NLP and tokenization\n* [tldts](https://github.com/remusao/tldts) - TLD parsing\n* [confusables](https://github.com/gc/confusables) - Unicode confusables detection\n\n---\n\n> Made with ❤️ by the [Forward Email](https://forwardemail.net) team\n\n\n##\n\n\"#\"\n","readmeFilename":"README.md"}