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Cyber_Simulator

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GAlbayrak commited on Jul 25, 2024

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76c0b7a

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1 Parent(s): 39a2303

Update app.py

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app.py +73 -73

app.py CHANGED Viewed

@@ -1,84 +1,84 @@
 #!/usr/bin/env python
 # coding: utf-8
-import os
-from flask import Flask, render_template, jsonify
-import random
-# Dizinlerin oluşturulması
-os.makedirs('cyber_security_platform/app/templates', exist_ok=True)
-# HTML dosyasının oluşturulması ve içeriğin yazılması
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>Cyber Security Platform</title>
-    <link rel="stylesheet" href="{{ url_for('static', filename='css/styles.css') }}">
-</head>
-<body>
-    <header>
-        <h1>Cyber Security AI Simulation</h1>
-        <nav>
-            <ul>
-                <li><a href="#about">About</a></li>
-                <li><a href="#features">Features</a></li>
-                <li><a href="#contact">Contact</a></li>
-            </ul>
-        </nav>
-    </header>
-    <main>
-        <section id="about">
-            <h2>About the Simulation</h2>
-            <p>Welcome to the Cyber Security AI Simulation. This platform is designed to help you learn and practice cyber security techniques using the power of artificial intelligence.</p>
-        </section>
-        <section id="features">
-            <h2>Features</h2>
-            <ul>
-                <li>Real-time threat detection</li>
-                <li>Interactive learning modules</li>
-                <li>AI-driven security analysis</li>
-            </ul>
-        </section>
-        <section id="start-simulation">
-            <h2>Start the Simulation</h2>
-            <button onclick="startSimulation()">Start Simulation</button>
-            <div id="simulation-result"></div>
-        </section>
-    </main>
-    <footer>
-        <p>&copy; 2024 Cyber Security AI Simulation. All rights reserved.</p>
-    </footer>
-    <script src="{{ url_for('static', filename='js/scripts.js') }}"></script>
-</body>
-</html>
-with open('cyber_security_platform/app/templates/index.html', 'w') as file:
-    file.write(html_content)
-# Flask uygulaması için gerekli kodlar
 app = Flask(__name__)
-# Tehdit türlerini belirleme işlevi
-def generate_threat():
-    threats = [
-        "DDoS Attack",
-        "Data Breach",
-        "Malware Infection",
-        "Phishing Attempt",
-        "SQL Injection"
-    ]
-    return random.choice(threats)
 @app.route('/')
-def home():
     return render_template('index.html')
-@app.route('/start_simulation')
-def start_simulation():
-    detected_threat = generate_threat()
-    return jsonify(result=detected_threat)
 if __name__ == '__main__':
-    app.run(debug=True, host='0.0.0.0')

 #!/usr/bin/env python
 # coding: utf-8
+# In[1]:
+import pandas as pd
+import numpy as np
+# Örnek veri seti oluşturma
+np.random.seed(42)  # Tekrarlanabilir sonuçlar için
+# Özellikler ve etiketler oluşturma
+data = pd.DataFrame({
+    'feature1': np.random.rand(100),
+    'feature2': np.random.rand(100),
+    'target': np.random.randint(0, 2, 100)
+})
+# Veri setini kaydetme
+data.to_csv('data.csv', index=False)
+print("Örnek veri seti 'data.csv' dosyasına kaydedildi.")
+# In[2]:
+from sklearn.ensemble import RandomForestClassifier
+import pickle
+import numpy as np
+import pandas as pd
+# Örnek veri seti ve model eğitimi
+data = pd.read_csv('data.csv')
+X = data.drop('target', axis=1)
+y = data['target']
+model = RandomForestClassifier()
+model.fit(X, y)
+# Modeli kaydedin
+with open('static/model/ai_model.pkl', 'wb') as file:
+    pickle.dump(model, file)
+# In[3]:
+from flask import Flask, render_template, request, jsonify
+import pickle
+import numpy as np
+import pandas as pd
 app = Flask(__name__)
+# Yapay zeka modelini yükleyin
+with open('static/model/ai_model.pkl', 'rb') as file:
+    ai_model = pickle.load(file)
 @app.route('/')
+def index():
     return render_template('index.html')
+@app.route('/simulate', methods=['POST'])
+def simulate():
+    user_input = request.json
+    # Yapay zeka modelini kullanarak tahmin yapın
+    features = np.array([user_input['data']]).reshape(1, -1)
+    prediction = ai_model.predict(features)[0]
+    return jsonify({'prediction': prediction})
+@app.route('/result')
+def result():
+    return render_template('result.html')
 if __name__ == '__main__':
+    app.run(debug=True)
+# In[ ]: