เอกสารอ้างอิง API

import SpamScanner from 'spamscanner';             // ESM
const SpamScanner = require('spamscanner');        // CommonJS

const scanner = new SpamScanner(options);
const result = await scanner.scan(rawMessage, scanOptions);

await scanner.scan(raw, {
  session: {
    remoteAddress: '203.0.113.5',             // the client's IP address
    resolvedClientHostname: 'mx.example.com', // its verified reverse DNS name
    helo: 'mx.example.com',
    envelope: {
      mailFrom: {address: 'alice@example.com'},
      rcptTo: [{address: 'bob@example.org'}],
    },
  },
});

import {authenticate} from 'mailauth';

const auth = await authenticate(raw, {ip, helo, sender, mta});
await scanner.scan(raw, {authenticationResults: auth});

{
  isSpam: true,
  score: 16.25,
  threshold: 5,
  rejectThreshold: 15,
  action: 'reject',                       // 'accept', 'tag' or 'reject'
  message: 'Spam (BAYES_999, PHISHING_LOOKALIKE_DOMAIN, DECEPTIVE_LINK, FROM_NAME_BRAND)',
  tests: [
    {name: 'BAYES_999', score: 6.25, description: 'Classifier spam probability 99.9%'},
    {name: 'PHISHING_LOOKALIKE_DOMAIN', score: 5, description: '"paypa1-secure.top" imitates paypal by swapping characters'},
    // ...
  ],
  results: {
    classification: {probability, category, spam, ham, clues, coverage},
    phishing: [],        // lookalike domains, deceptive links, Cloudflare and URIBL findings
    uncheckedHosts: 0,   // link hosts beyond phishing.maxHosts, not looked up
    attachments: [],     // every attachment finding
    executables: [],     // the executable findings among them
    macros: [],          // macros, active PDFs, RTF objects
    viruses: [],         // ClamAV findings: {filename, virus: [names], message}
    arbitrary: [],       // rules strong enough to mark spam alone
    obfuscation: {invisible, mixed, styled},
    authentication: null, // mailauth's results with a score, when checked
    reputation: null,
    dnsbl: [],           // {zone, value} for each listing
    language: {language, script, notAllowed},
    llm: null,           // the language model's verdict, when asked
    toxicity: [],
    nsfw: [],
    idnHomographAttack: {detected, domains, riskScore},
  },
  links: ['http://paypa1-secure.top/login'],
  language: 'en',
  tokens: ['dear', 'customer', /* ... */],
  mail: {/* the parsed message, from mailparser */},
  version: '7.0.0',
  metrics: {totalTime: 42},
}

import SpamScanner, {
  Classifier, getFeatures, segmentWords, detectLanguage,
  LLMClassifier, PROVIDERS, RECOMMENDED_MODELS, CLASSIFIER_MODELS,
  DEFAULT_SCORES, scoreResults, spamHeaders, rewriteMessage,
  loadModel, saveModel, defaultModelPath, loadDefaultModel,
  train, evaluate, readExamples,
  MilterServer, createHttpServer, createTcpServer, createSpamdServer,
  DEFAULTS, VERSION,
} from 'spamscanner';

import ArfParser from 'spamscanner/arf';

const classifier = new Classifier({spamCutoff: 0.99, hamCutoff: 0.2});
classifier.learn(getFeatures(mail).features, 'spam');
classifier.classify(features); // {probability, category, spam, ham, clues, coverage}

import {train, evaluate, saveModel} from 'spamscanner';

const {classifier, spam, ham} = await train({
  spam: ['/mail/Junk'],
  ham: ['/mail/Archive', '/mail/archive.mbox'],
  datasets: [{file: 'extra.csv', textColumn: 'body', labelColumn: 'kind'}],
});
saveModel(classifier, 'model.json');

import SpamScanner, {MilterServer, createHttpServer, createSpamdServer} from 'spamscanner';

const scanner = new SpamScanner({authentication: true});
const milter = new MilterServer(scanner, {reject: true, rejectCode: 451, subjectTag: '[SPAM]'});
milter.on('scan', ({session, result}) => console.log(session.remoteAddress, result.score));
await milter.listen(7831);

createHttpServer(scanner, {token: process.env.SPAMSCANNER_TOKEN}).listen(7832, '127.0.0.1');
createSpamdServer(scanner).listen(783, '127.0.0.1');

import ArfParser from 'spamscanner/arf';

const report = await ArfParser.parse(rawReport);
console.log(report.feedbackType, report.sourceIp, report.originalHeaders.subject);

const raw = ArfParser.create({
  feedbackType: 'abuse', userAgent: 'MyService/1.0',
  from: 'abuse@example.com', to: 'fbl@example.net',
  originalMessage, sourceIp: '192.0.2.1',
});