Update README.md

README.md

@@ -1,168 +1,181 @@
-# Abdal XSS AI Engine
-
-## 🎤 README Translation
-- [English](README.md)
-- [فارسی](README.fa.md)
-
- 
-
-<p align="center"><img src="scr.jpg?raw=true"></p>
-
-
-## 💎 General purpose
-The Abdal XSS AI Engine was developed to provide a free and advanced solution for combating XSS attacks, particularly in Iran, where there is a lack of local cybersecurity models. This AI-based model addresses the crucial need for enhanced cybersecurity and aims to protect users by preventing XSS attacks more effectively.
-
-
-## 🛠️ Development Environment Setup
-- **Python 3.7 or higher**
-- **Flask** (for building RESTful APIs)
-- **TensorFlow** (Deep Learning models)
-- **Scikit-learn** (for text preprocessing and TF-IDF vectorization)
-- **Pandas** (for large-scale data management)
-- **Deep understanding of web security and XSS attacks**
-- **Git** (version control and repository management)
-
-
-
-### 🔥 Requirements
-
-- **Python 3.7 or higher**
-- **Flask**
-- **TensorFlow**
-- **Scikit-learn**
-- **Pandas**
-- **Pickle**
-
-
-## ✨ Features
-
-- Ability to process and detect hundreds of thousands of XSS patterns, including new emerging threats.
-- Utilizes a deep learning model with multiple Dense and Dropout layers.
-- Trained using a combined dataset from multiple CSV files.
-- Employs TF-IDF technique for text feature extraction from XSS attacks.
-- Capability to improve model accuracy with new data and continuous updates.
-- Supports model optimization using the Adam optimizer and accuracy metrics.
-- Saves the final model and vectorizer for future use and deployment in various environments.
-
-
-## 📝️ How it Works?
- 
-You can use the model as an API for detecting XSS attacks by using the following code:
-```python
-from flask import Flask, request, jsonify
-import tensorflow as tf
-import pickle
-import numpy as np
-
-app = Flask(__name__)
-
-# Load the model and vectorizer
-model = tf.keras.models.load_model('Abdal_XSS_AI_Engine.h5')
-with open('vectorizer.pkl', 'rb') as f:
-    vectorizer = pickle.load(f)
-
-
-@app.route('/predict', methods=['POST'])
-def predict():
-    data = request.json
-    sentences = data['sentences']
-
-    # Preprocess the input data using the vectorizer
-    X_new = vectorizer.transform(sentences).toarray()
-
-    # Make predictions
-    predictions = (model.predict(X_new) > 0.5).astype(int)
-
-    # Prepare and return the response
-    response = {
-        'predictions': ['XSS Detected' if pred == 1 else 'No XSS Detected' for pred in predictions.flatten()]
-    }
-    return jsonify(response)
-
-
-if __name__ == '__main__':
-    app.run(debug=True)
-
-```
-In addition to the API, you can also use the model to read data from a text file and detect attacks. The following code is an example of this use case:
-
-```python
-import os
-import tensorflow as tf
-import pickle
-
-# Disable oneDNN custom operations
-os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
-# Set TensorFlow logging level to 'ERROR' to suppress the info and warning messages
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
-
-# Check if model and vectorizer files exist
-model_path = 'Abdal_XSS_AI_Engine.keras'
-vectorizer_path = 'vectorizer.pkl'
-
-if not os.path.exists(model_path):
-    raise FileNotFoundError(f"Model file not found: {model_path}")
-if not os.path.exists(vectorizer_path):
-    raise FileNotFoundError(f"Vectorizer file not found: {vectorizer_path}")
-
-# Load the model from the Keras format
-model_name = "Abdal XSS AI Engine"
-model = tf.keras.models.load_model(model_path)
-
-# Load the vectorizer
-with open(vectorizer_path, 'rb') as f:
-    vectorizer = pickle.load(f)
-
-# Read new data (sentences) from a file (e.g., 'attack-xss-payload.txt')
-input_file = 'attack-xss-payload.txt'
-if not os.path.exists(input_file):
-    raise FileNotFoundError(f"Input file not found: {input_file}")
-
-with open(input_file, 'r', encoding='utf-8') as file:
-    new_sentences = [line.strip() for line in file if line.strip()]  # Reading each line from file
-
-# Check if any sentence exists for prediction
-if not new_sentences:
-    raise ValueError("No data available for prediction.")
-
-# Preprocess the new data using the loaded TF-IDF vectorizer
-X_new = vectorizer.transform(new_sentences).toarray()
-
-# Predict using the loaded model
-predictions = (model.predict(X_new) > 0.5).astype(int)
-
-# Print predictions
-for i, sentence in enumerate(new_sentences):
-    print(f"Sentence: {sentence}")
-    print(f"Prediction: {'XSS Detected' if predictions[i] == 1 else 'No XSS Detected'}\n")
-
-```
-### Abdal XSS AI Engine - TensorFlow Model
-
-To use the trained model in your TensorFlow project, simply run the following Python code:
-
-```python
-import tensorflow as tf
-
-# Load the saved model
-model = tf.keras.models.load_model('Abdal_XSS_AI_Engine')
-
-print("✅ Model loaded successfully!")
-```
-
-## ❤️ Donation
-
-https://ebrasha.com/abdal-donation
-
-## 🤵 Programmer
-Handcrafted with Passion by Ebrahim Shafiei (EbraSha)
-
-E-Mail = [email protected]
-
-Telegram: https://t.me/ProfShafiei
-
-## ☠️ Reporting Issues
-
-If you are facing a configuration issue or something is not working as you expected to be, please use the **[email protected]** . Issues on GitLab  or Github are also welcomed.
-
-
+---
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+pipeline_tag: text-classification
+tags:
+- abdal
+- xss
+- hack
+- ebrasha
+- ai
+- tensorflow
+---
+# Abdal XSS AI Engine
+
+## 🎤 README Translation
+- [English](README.md)
+- [فارسی](README.fa.md)
+
+ 
+
+<p align="center"><img src="scr.jpg?raw=true"></p>
+
+
+## 💎 General purpose
+The Abdal XSS AI Engine was developed to provide a free and advanced solution for combating XSS attacks, particularly in Iran, where there is a lack of local cybersecurity models. This AI-based model addresses the crucial need for enhanced cybersecurity and aims to protect users by preventing XSS attacks more effectively.
+
+
+## 🛠️ Development Environment Setup
+- **Python 3.7 or higher**
+- **Flask** (for building RESTful APIs)
+- **TensorFlow** (Deep Learning models)
+- **Scikit-learn** (for text preprocessing and TF-IDF vectorization)
+- **Pandas** (for large-scale data management)
+- **Deep understanding of web security and XSS attacks**
+- **Git** (version control and repository management)
+
+
+
+### 🔥 Requirements
+
+- **Python 3.7 or higher**
+- **Flask**
+- **TensorFlow**
+- **Scikit-learn**
+- **Pandas**
+- **Pickle**
+
+
+## ✨ Features
+
+- Ability to process and detect hundreds of thousands of XSS patterns, including new emerging threats.
+- Utilizes a deep learning model with multiple Dense and Dropout layers.
+- Trained using a combined dataset from multiple CSV files.
+- Employs TF-IDF technique for text feature extraction from XSS attacks.
+- Capability to improve model accuracy with new data and continuous updates.
+- Supports model optimization using the Adam optimizer and accuracy metrics.
+- Saves the final model and vectorizer for future use and deployment in various environments.
+
+
+## 📝️ How it Works?
+ 
+You can use the model as an API for detecting XSS attacks by using the following code:
+```python
+from flask import Flask, request, jsonify
+import tensorflow as tf
+import pickle
+import numpy as np
+
+app = Flask(__name__)
+
+# Load the model and vectorizer
+model = tf.keras.models.load_model('Abdal_XSS_AI_Engine.h5')
+with open('vectorizer.pkl', 'rb') as f:
+    vectorizer = pickle.load(f)
+
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    data = request.json
+    sentences = data['sentences']
+
+    # Preprocess the input data using the vectorizer
+    X_new = vectorizer.transform(sentences).toarray()
+
+    # Make predictions
+    predictions = (model.predict(X_new) > 0.5).astype(int)
+
+    # Prepare and return the response
+    response = {
+        'predictions': ['XSS Detected' if pred == 1 else 'No XSS Detected' for pred in predictions.flatten()]
+    }
+    return jsonify(response)
+
+
+if __name__ == '__main__':
+    app.run(debug=True)
+
+```
+In addition to the API, you can also use the model to read data from a text file and detect attacks. The following code is an example of this use case:
+
+```python
+import os
+import tensorflow as tf
+import pickle
+
+# Disable oneDNN custom operations
+os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
+# Set TensorFlow logging level to 'ERROR' to suppress the info and warning messages
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+
+# Check if model and vectorizer files exist
+model_path = 'Abdal_XSS_AI_Engine.keras'
+vectorizer_path = 'vectorizer.pkl'
+
+if not os.path.exists(model_path):
+    raise FileNotFoundError(f"Model file not found: {model_path}")
+if not os.path.exists(vectorizer_path):
+    raise FileNotFoundError(f"Vectorizer file not found: {vectorizer_path}")
+
+# Load the model from the Keras format
+model_name = "Abdal XSS AI Engine"
+model = tf.keras.models.load_model(model_path)
+
+# Load the vectorizer
+with open(vectorizer_path, 'rb') as f:
+    vectorizer = pickle.load(f)
+
+# Read new data (sentences) from a file (e.g., 'attack-xss-payload.txt')
+input_file = 'attack-xss-payload.txt'
+if not os.path.exists(input_file):
+    raise FileNotFoundError(f"Input file not found: {input_file}")
+
+with open(input_file, 'r', encoding='utf-8') as file:
+    new_sentences = [line.strip() for line in file if line.strip()]  # Reading each line from file
+
+# Check if any sentence exists for prediction
+if not new_sentences:
+    raise ValueError("No data available for prediction.")
+
+# Preprocess the new data using the loaded TF-IDF vectorizer
+X_new = vectorizer.transform(new_sentences).toarray()
+
+# Predict using the loaded model
+predictions = (model.predict(X_new) > 0.5).astype(int)
+
+# Print predictions
+for i, sentence in enumerate(new_sentences):
+    print(f"Sentence: {sentence}")
+    print(f"Prediction: {'XSS Detected' if predictions[i] == 1 else 'No XSS Detected'}\n")
+
+```
+### Abdal XSS AI Engine - TensorFlow Model
+
+To use the trained model in your TensorFlow project, simply run the following Python code:
+
+```python
+import tensorflow as tf
+
+# Load the saved model
+model = tf.keras.models.load_model('Abdal_XSS_AI_Engine')
+
+print("✅ Model loaded successfully!")
+```
+
+## ❤️ Donation
+
+https://ebrasha.com/abdal-donation
+
+## 🤵 Programmer
+Handcrafted with Passion by Ebrahim Shafiei (EbraSha)
+
+E-Mail = [email protected]
+
+Telegram: https://t.me/ProfShafiei
+
+## ☠️ Reporting Issues
+
+If you are facing a configuration issue or something is not working as you expected to be, please use the **[email protected]** . Issues on GitLab  or Github are also welcomed.