Rename app.py to Sniffer_AI(GPS Tracker Dataset).py

Sniffer_AI(GPS Tracker Dataset).py

@@ -0,0 +1,97 @@
+import pandas as pd
+import numpy as np
+import joblib
+import gradio as gr
+import os
+import tempfile
+
+# Set a custom directory for Gradio's temporary files
+os.environ["GRADIO_TEMP"] = tempfile.mkdtemp()
+
+# Load the saved Random Forest model
+rf_model = joblib.load('rf_model.pkl')  # Ensure the correct model path
+
+# Define required numeric features
+numeric_features = [
+    "date_numeric", "time_numeric", "door_state", "sphone_signal", "label"
+]
+
+# Class labels for attack types
+class_labels = {
+    0: "Normal",
+    1: "Backdoor",
+    2: "DDoS",
+    3: "Injection",
+    4: "Password Attack",
+    5: "Ransomware",
+    6: "Scanning",
+    7: "XSS",
+}
+
+def convert_datetime_features(log_data):
+    """Convert date and time into numeric values."""
+    try:
+        log_data['date'] = pd.to_datetime(log_data['date'], format='%d-%m-%y', errors='coerce')
+        log_data['date_numeric'] = log_data['date'].astype(np.int64) // 10**9  
+
+        time_parsed = pd.to_datetime(log_data['time'], format='%H:%M:%S', errors='coerce')
+        log_data['time_numeric'] = (time_parsed.dt.hour * 3600) + (time_parsed.dt.minute * 60) + time_parsed.dt.second
+    except Exception as e:
+        return f"Error processing date/time: {str(e)}"
+    
+    return log_data
+
+def detect_intrusion(file):
+    """Process log file and predict attack type."""
+    try:
+        log_data = pd.read_csv(file.name)
+    except Exception as e:
+        return f"Error reading file: {str(e)}"
+
+    log_data = convert_datetime_features(log_data)
+
+    missing_features = [feature for feature in numeric_features if feature not in log_data.columns]
+    if missing_features:
+        return f"Missing features in file: {', '.join(missing_features)}"
+
+    try:
+        log_data['door_state'] = log_data['door_state'].astype(str).str.strip().replace({'closed': 0, 'open': 1})
+        log_data['sphone_signal'] = pd.to_numeric(log_data['sphone_signal'], errors='coerce')
+
+        feature_values = log_data[numeric_features].astype(float).values
+        predictions = rf_model.predict(feature_values)
+    except Exception as e:
+        return f"Error during prediction: {str(e)}"
+
+    # Map predictions to specific attack types
+    log_data['Prediction'] = [class_labels.get(pred, 'Unknown Attack') for pred in predictions]
+
+    # Format date for output
+    log_data['date'] = log_data['date'].dt.strftime('%Y-%m-%d')
+
+    # Select final output columns
+    output_df = log_data[['date', 'time', 'Prediction']]
+
+    # Save the output to a CSV file for download
+    output_file = "intrusion_results.csv"
+    output_df.to_csv(output_file, index=False)
+
+    return output_df, output_file
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=detect_intrusion,
+    inputs=[gr.File(label="Upload Log File (CSV format)")],
+    outputs=[gr.Dataframe(label="Intrusion Detection Results"), gr.File(label="Download Predictions CSV")],
+    title="Intrusion Detection System",
+    description=(
+        """
+        Upload a CSV log file with the following features:
+        date,time,door_state,sphone_signal,label
+        Example:
+        26-04-19,13:59:20,1,-85,normal
+        """
+    )
+)
+
+iface.launch()

app.py

@@ -1,64 +0,0 @@
-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('decision_tree_model.pkl')
-bagging_model = joblib.load('model_bagging.pkl')
-ada_model = joblib.load('model_adaboost.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 = decision_tree_model
-    elif model_choice == "Bagging Classifier":
-        model = model_bagging
-    elif model_choice == "AdaBoost Classifier":
-        model = model_adaboost
-    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()