How can message-based phishing attacks be detected programmatically

Question

I’m exploring ways to detect phishing attacks within a messaging application. Since phishing attacks often involve deceptive links or malicious attachments, I’m wondering if there are methods or algorithms to identify these risks programmatically.

Could someone share common approaches for flagging phishing attempts based on message content, link analysis, or user patterns? If there are specific tools or libraries for this, I’d be interested in learning more.

Answer 1

In order to detect message-based phishing attacks programmatically, we can follow the following approaches:

1. URL Analysis with Google Safe Browsing

We can use Google Safe Browsing API to check if URLs are malicious.

const fetch = require('node-fetch');

async function checkURL(url) {
    const apiKey = 'YOUR_API_KEY';
    const apiURL = `https://safebrowsing.googleapis.com/v4/threatMatches:find?key=${apiKey}`;
    const body = {
        client: {
            clientId: "your-app",
            clientVersion: "1.0"
        },
        threatInfo: {
            threatTypes: ["MALWARE", "SOCIAL_ENGINEERING"],
            platformTypes: ["ANY_PLATFORM"],
            threatEntryTypes: ["URL"],
            threatEntries: [{ url: url }]
        }
    };
    const response = await fetch(apiURL, {
        method: 'POST',
        body: JSON.stringify(body),
        headers: { 'Content-Type': 'application/json' }
    });
    const data = await response.json();
    return data.matches ? true : false; // true if malicious
}

checkURL("http://suspicious-url.com").then(isMalicious => {
    if (isMalicious) console.log("Phishing link detected!");
    else console.log("URL is safe.");
});

2. Text Analysis with NLP

We can use simple keyword-based detection to catch common phishing terms.

function containsPhishingKeywords(message) {
    const phishingKeywords = ["urgent", "verify your account", "limited time"];
    return phishingKeywords.some(keyword => message.toLowerCase().includes(keyword));
}

const message = "Please verify your account urgently to avoid suspension.";
if (containsPhishingKeywords(message)) {
    console.log("Potential phishing message detected!");
}

3. Machine Learning with TensorFlow.js

We can train models on phishing data using features like link count, attachment presence, and urgency.

This is a basic setup for checking the likelihood of phishing. For full model training, you would need a pre-labeled dataset.

const tf = require('@tensorflow/tfjs-node');

async function classifyMessage(message) {
    const model = await tf.loadLayersModel('path/to/model.json');
    const inputTensor = tf.tensor([message.length, (message.match(/\./g) || []).length]); // Example features: message length, periods
    const prediction = model.predict(inputTensor.expandDims(0));
    const isPhishing = prediction.dataSync()[0] > 0.5; // threshold example
    return isPhishing;
}

classifyMessage("Suspicious message text").then(result => {
    console.log(result ? "Phishing detected!" : "Message seems safe.");
});

4. Attachment Scanning

We can block specific file types to prevent malicious attachment phishing.

const blockedExtensions = ['.exe', '.js', '.vbs'];

function isSuspiciousAttachment(filename) {
    return blockedExtensions.some(ext => filename.endsWith(ext));
}

const filename = 'suspicious.js';
if (isSuspiciousAttachment(filename)) {
    console.log("Potentially dangerous attachment detected!");
}

5. Behavioral Analysis

We can set up rate limiting based on IP addresses or user activity.

const rateLimit = {};
const MAX_REQUESTS = 5; // allowed requests per minute

function rateLimitCheck(userId) {
    if (!rateLimit[userId]) {
        rateLimit[userId] = { count: 1, timestamp: Date.now() };
    } else {
        const timeElapsed = Date.now() - rateLimit[userId].timestamp;
        if (timeElapsed < 60000) {
            rateLimit[userId].count++;
        } else {
            rateLimit[userId] = { count: 1, timestamp: Date.now() };
        }
    }

    if (rateLimit[userId].count > MAX_REQUESTS) {
        console.log("Unusual behavior detected! Possible phishing attempt.");
        return true;
    }
    return false;
}

// Usage
if (rateLimitCheck("user123")) {
    console.log("Warning: Too many messages from user123.");
}

Comments

Using TensorFlow.js for phishing detection is a great idea. Perhaps adding details about dataset preparation or feature engineering could help readers implement it more effectively.