Scam detection ai

.github/workflows/generator-generic-ossf-slsa3-publish.yml

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+# This workflow uses actions that are not certified by GitHub.
+# They are provided by a third-party and are governed by
+# separate terms of service, privacy policy, and support
+# documentation.
+
+# This workflow lets you generate SLSA provenance file for your project.
+# The generation satisfies level 3 for the provenance requirements - see https://slsa.dev/spec/v0.1/requirements
+# The project is an initiative of the OpenSSF (openssf.org) and is developed at
+# https://github.com/slsa-framework/slsa-github-generator.
+# The provenance file can be verified using https://github.com/slsa-framework/slsa-verifier.
+# For more information about SLSA and how it improves the supply-chain, visit slsa.dev.
+
+name: SLSA generic generator
+on:
+  workflow_dispatch:
+  release:
+    types: [created]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    outputs:
+      digests: ${{ steps.hash.outputs.digests }}
+
+    steps:
+      - uses: actions/checkout@v4
+
+      # ========================================================
+      #
+      # Step 1: Build your artifacts.
+      #
+      # ========================================================
+      - name: Build artifacts
+        run: |
+            # These are some amazing artifacts.
+            echo "artifact1" > artifact1
+            echo "artifact2" > artifact2
+
+      # ========================================================
+      #
+      # Step 2: Add a step to generate the provenance subjects
+      #         as shown below. Update the sha256 sum arguments
+      #         to include all binaries that you generate
+      #         provenance for.
+      #
+      # ========================================================
+      - name: Generate subject for provenance
+        id: hash
+        run: |
+          set -euo pipefail
+
+          # List the artifacts the provenance will refer to.
+          files=$(ls artifact*)
+          # Generate the subjects (base64 encoded).
+          echo "hashes=$(sha256sum $files | base64 -w0)" >> "${GITHUB_OUTPUT}"
+
+  provenance:
+    needs: [build]
+    permissions:
+      actions: read   # To read the workflow path.
+      id-token: write # To sign the provenance.
+      contents: write # To add assets to a release.
+    uses: slsa-framework/slsa-github-generator/.github/workflows/generator_generic_slsa3.yml@v1.4.0
+    with:
+      base64-subjects: "${{ needs.build.outputs.digests }}"
+      upload-assets: true # Optional: Upload to a new release

collections/scam-detection-ai/app.py

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+from flask import Flask, render_template, request
+from email_analyzer import analyze_email
+from email_fetcher import fetch_emails
+from message_analyzer import analyze_message
+
+app = Flask(__name__)
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/analyze', methods=['POST'])
+def analyze():
+    if 'email_text' in request.form:
+        email_text = request.form['email_text']
+        analysis = analyze_email(email_text)
+        total_score = sum(analysis.values())
+        is_scam = total_score >= 3
+        return render_template('result.html', analysis=analysis, is_scam=is_scam, analysis_type='Email')
+
+    elif 'message_text' in request.form:
+        message_text = request.form['message_text']
+        analysis = analyze_message(message_text)
+        total_score = sum(analysis.values())
+        is_scam = total_score >= 2
+        return render_template('result.html', analysis=analysis, is_scam=is_scam, analysis_type='Message')
+
+    elif 'fetch_emails' in request.form:
+        username = request.form['username']
+        password = request.form['password']
+        emails = fetch_emails(username, password)
+
+        analyzed_emails = []
+        if emails:
+            for email_message in emails:
+                body = ""
+                if email_message.is_multipart():
+                    for part in email_message.walk():
+                        if part.get_content_type() == "text/plain":
+                            body = part.get_payload(decode=True).decode()
+                            break
+                else:
+                    body = email_message.get_payload(decode=True).decode()
+
+                analysis = analyze_email(body)
+                total_score = sum(analysis.values())
+                is_scam = total_score >= 3
+
+                subject, _ = email.header.decode_header(email_message["Subject"])[0]
+                if isinstance(subject, bytes):
+                    subject = subject.decode()
+
+                analyzed_emails.append({
+                    "subject": subject,
+                    "from": email_message['From'],
+                    "analysis": analysis,
+                    "is_scam": is_scam
+                })
+        return render_template('results_list.html', emails=analyzed_emails)
+
+    return "Invalid request", 400
+
+if __name__ == '__main__':
+    app.run(debug=True)

collections/scam-detection-ai/chrome-extension/background.js

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+chrome.runtime.onInstalled.addListener(() => {
+  chrome.contextMenus.create({
+    id: "analyzeText",
+    title: "Analyze for Scams",
+    contexts: ["selection"]
+  });
+});
+
+chrome.contextMenus.onClicked.addListener((info, tab) => {
+  if (info.menuItemId === "analyzeText") {
+    fetch('http://localhost:5000/analyze', {
+        method: 'POST',
+        headers: {
+            'Content-Type': 'application/x-www-form-urlencoded',
+        },
+        body: `message_text=${encodeURIComponent(info.selectionText)}`
+    })
+    .then(response => response.text())
+    .then(data => {
+        chrome.storage.local.set({ analysisResult: data });
+    })
+    .catch(error => {
+        console.error('Error:', error);
+        chrome.storage.local.set({ analysisResult: 'Error analyzing text.' });
+    });
+  }
+});

collections/scam-detection-ai/chrome-extension/manifest.json

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+{
+  "manifest_version": 3,
+  "name": "Scam Detection AI",
+  "version": "1.0",
+  "description": "Analyzes selected text for scams.",
+  "permissions": [
+    "activeTab",
+    "contextMenus"
+  ],
+  "background": {
+    "service_worker": "background.js"
+  },
+  "action": {
+    "default_popup": "popup.html"
+  }
+}

collections/scam-detection-ai/chrome-extension/popup.html

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+<!DOCTYPE html>
+<html>
+<head>
+    <title>Scam Detection AI</title>
+    <style>
+        body { width: 300px; font-family: sans-serif; }
+        textarea { width: 100%; height: 100px; }
+    </style>
+</head>
+<body>
+    <h1>Scam Detection AI</h1>
+    <textarea id="textToAnalyze" placeholder="Text to analyze..."></textarea>
+    <button id="analyzeButton">Analyze</button>
+    <div id="result"></div>
+    <script src="popup.js"></script>
+</body>
+</html>

collections/scam-detection-ai/chrome-extension/popup.js

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+document.addEventListener('DOMContentLoaded', () => {
+    chrome.storage.local.get(['analysisResult'], (result) => {
+        if (result.analysisResult) {
+            document.getElementById('result').innerHTML = result.analysisResult;
+            chrome.storage.local.remove(['analysisResult']);
+        }
+    });
+});
+
+document.getElementById('analyzeButton').addEventListener('click', () => {
+    const text = document.getElementById('textToAnalyze').value;
+    fetch('http://localhost:5000/analyze', {
+        method: 'POST',
+        headers: {
+            'Content-Type': 'application/x-www-form-urlencoded',
+        },
+        body: `message_text=${encodeURIComponent(text)}`
+    })
+    .then(response => response.text())
+    .then(data => {
+        document.getElementById('result').innerHTML = data;
+    })
+    .catch(error => {
+        console.error('Error:', error);
+        document.getElementById('result').innerText = 'Error analyzing text.';
+    });
+});

collections/scam-detection-ai/email_analyzer.py

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+import re
+
+import requests
+
+def get_domain_reputation(domain):
+    """
+    Checks the reputation of a domain using a mock API.
+    In a real application, this would integrate with a service like VirusTotal.
+    """
+    # This is a mock response.
+    if "fake" in domain or "suspicious" in domain:
+        return "Malicious"
+    return "Clean"
+
+def analyze_email(email_text):
+    """
+    Analyzes an email for potential scam indicators.
+
+    Args:
+        email_text: The full text of the email.
+
+    Returns:
+        A dictionary containing the analysis results.
+    """
+    scam_indicators = {
+        "urgency": 0,
+        "generic_greeting": 0,
+        "suspicious_links": 0,
+        "unusual_sender": 0,
+        "payment_requests": 0,
+        "attachments": 0,
+        "domain_analysis": {}
+    }
+
+    # Urgency keywords
+    urgency_keywords = ["urgent", "immediate", "action required", "limited time", "expire"]
+    for keyword in urgency_keywords:
+        if re.search(r'\b' + keyword + r'\b', email_text, re.IGNORECASE):
+            scam_indicators["urgency"] = 1
+            break
+
+    # Generic greeting
+    generic_greetings = ["dear customer", "dear user", "sir/madam"]
+    for greeting in generic_greetings:
+        if greeting in email_text.lower():
+            scam_indicators["generic_greeting"] = 1
+            break
+
+    # Suspicious links (basic check for non-standard URLs)
+    if re.search(r'http[s]?://[a-zA-Z0-9.-]+\.[a-zA-Z]{2,6}/[a-zA-Z0-9_.-]+', email_text):
+        scam_indicators["suspicious_links"] = 1
+
+    # Unusual sender (very basic check)
+    if "From:" in email_text:
+        from_line = email_text.split("From:")[1].split("\n")[0]
+        if re.search(r'<.*@.*>', from_line) and not re.search(r'<.*@.*\..*>', from_line):
+            scam_indicators["unusual_sender"] = 1
+
+    # Payment requests
+    payment_keywords = ["payment", "invoice", "wire transfer", "bank account", "credit card"]
+    for keyword in payment_keywords:
+        if re.search(r'\b' + keyword + r'\b', email_text, re.IGNORECASE):
+            scam_indicators["payment_requests"] = 1
+            break
+
+    # Attachments
+    if "Content-Disposition: attachment" in email_text:
+        scam_indicators["attachments"] = 1
+
+    # Domain analysis
+    urls = re.findall(r'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', email_text)
+    domains = [re.search(r'//(.*?)/', url).group(1) for url in urls if re.search(r'//(.*?)/', url)]
+
+    for domain in domains:
+        reputation = get_domain_reputation(domain)
+        scam_indicators["domain_analysis"][domain] = reputation
+
+    return scam_indicators
+
+if __name__ == '__main__':
+    sample_email = """
+    From: Suspicious Sender <suspicious@example.com>
+    Subject: Urgent Action Required: Your Account Will Be Deactivated
+
+    Dear user,
+
+    We have detected suspicious activity on your account. For your security, we have temporarily suspended your account.
+    To reactivate your account, you must verify your identity by clicking the link below and updating your payment information.
+
+    http://suspicious-link.com/verify
+
+    Failure to do so within 24 hours will result in permanent account deactivation.
+
+    Thank you for your cooperation.
+
+    Sincerely,
+    The Security Team
+    """
+
+    analysis = analyze_email(sample_email)
+    print(analysis)
+
+    total_score = sum(analysis.values())
+    if total_score >= 3:
+        print("\nThis email is likely a scam!")
+    else:
+        print("\nThis email seems legitimate.")