initial commit

.gitignore

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+flagged/
+*.pt
+*.png
+*.jpg
+*.mkv
+*.mp4
+gradio_cached examples/

Sniffer_AI.py

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+import gradio as gr
+import joblib
+import requests
+import os
+
+from sklearn.ensemble import RandomForestClassifier, BaggingClassifier, AdaBoostClassifier
+from sklearn.tree import DecisionTreeClassifier
+
+
+# Load the saved models
+rf_model = joblib.load('rf_model.pkl')
+dt_model = joblib.load('dt_model.pkl')
+bagging_model = joblib.load('bagging_model.pkl')
+ada_model = joblib.load('ada_model.pkl')
+
+class_labels = {
+    0: "normal",
+    1: "backdoor",
+    2: "ddos",
+    3: "dos",
+    4: "injection",
+    5: "password",
+    6: "ransomware",
+    7: "scanning",
+    8: "xss",
+    9: "mitm"
+}
+
+def detect_intrusion(features, model_choice="Random Forest"):
+    # Convert the input string (comma-separated values) into a list of floats
+    features = [list(map(float, features.split(",")))]
+    
+    # Choose the model based on user selection
+    if model_choice == "Random Forest":
+        model = rf_model
+    elif model_choice == "Decision Tree":
+        model = dt_model
+    elif model_choice == "Bagging Classifier":
+        model = bagging_model
+    elif model_choice == "AdaBoost Classifier":
+        model = ada_model
+    else:
+        return "Invalid model choice!"
+    
+    # Predict the class (multi-class classification)
+    prediction = model.predict(features)
+    predicted_class = prediction[0]  # Get the predicted class (an integer between 0-8)
+    
+    # Return the human-readable class description
+    if predicted_class == 0:
+        return "No Intrusion Detected"
+    else:
+        return f"Intrusion Detected: {class_labels.get(predicted_class, 'Unknown Attack')}"
+
+# Create a Gradio interface
+iface = gr.Interface(fn=detect_intrusion, 
+                     inputs=[gr.Textbox(label="Input Features (comma-separated)"), 
+                             gr.Dropdown(choices=["Random Forest", "Decision Tree", "Bagging Classifier", "AdaBoost Classifier"], label="Select Model")], 
+                     outputs="text", 
+                     title="Intrusion Detection System",
+                     description="Enter features in the format: feature1, feature2, feature3...")
+
+# Launch the interface locally for testing
+iface.launch()

requirements.txt

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+# Core Libraries for Machine Learning
+scikit-learn==1.2.0          # Essential library for machine learning models (Random Forest, Decision Trees, etc.)
+numpy==1.23.0                # Numerical operations (required for model input/output processing)
+pandas==1.5.2                # Data manipulation and preprocessing
+
+# Plotting and Visualization Tools
+matplotlib==3.7.0            # Visualization library (used for plotting confusion matrices)
+seaborn==0.12.1              # Advanced data visualization, helpful for heatmaps (confusion matrix)
+
+# Saving and Loading Models
+joblib==1.2.0                # For saving and loading machine learning models (used for Random Forest, Decision Trees, etc.)
+
+# Reporting and Metrics
+scikit-learn==1.2.0          # For generating classification reports, confusion matrices, and model evaluation metrics
+
+# Hugging Face Hub Integration
+huggingface_hub==0.16.0      # Integration with Hugging Face Hub (for model uploading, downloading, sharing)
+transformers==4.26.1         # Hugging Face Transformers library (for model usage on the Hub)
+
+# Optional - Jupyter Notebooks for Model Development and Experimentation
+notebook==7.0.0              # For running Jupyter Notebooks in your project
+
+# Optional - TensorBoard for Visualizing Training Process (if applicable to larger models)
+tensorboard==2.10.1          # For tracking and visualizing model training
+
+# Extras for performance and speedups
+xgboost==1.6.2               # Gradient boosting library (optional, if you want to use advanced tree-based models)
+lightgbm==3.3.5              # LightGBM for fast gradient boosting (optional, for high performance)
+