Update Sniffer_AI(GPS Tracker Dataset).py

Sniffer_AI(GPS Tracker Dataset).py

@@ -8,12 +8,12 @@ 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
+# Load the trained Decision Tree model
+decision_tree_model = joblib.load('decision_tree_model.pkl')  # Update path if necessary
 
 # Define required numeric features
 numeric_features = [
-    "date_numeric", "time_numeric", "door_state", "sphone_signal", "label"
+    "date_numeric", "time_numeric", "latitude", "longitude", "label"
 ]
 
 # Class labels for attack types
@@ -31,18 +31,20 @@ class_labels = {
 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  
+        log_data['date'] = pd.to_datetime(log_data['date'], format='%d-%b-%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
+        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."""
+    """Process GPS tracker log file and predict attack type."""
     try:
         log_data = pd.read_csv(file.name)
     except Exception as e:
@@ -55,11 +57,8 @@ def detect_intrusion(file):
         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)
+        predictions = model.predict(feature_values)
     except Exception as e:
         return f"Error during prediction: {str(e)}"
 
@@ -70,10 +69,10 @@ def detect_intrusion(file):
     log_data['date'] = log_data['date'].dt.strftime('%Y-%m-%d')
 
     # Select final output columns
-    output_df = log_data[['date', 'time', 'Prediction']]
+    output_df = log_data[['date', 'time', 'latitude', 'longitude', 'Prediction']]
 
     # Save the output to a CSV file for download
-    output_file = "intrusion_results.csv"
+    output_file = "gps_intrusion_results.csv"
     output_df.to_csv(output_file, index=False)
 
     return output_df, output_file
@@ -81,15 +80,18 @@ def detect_intrusion(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",
+    inputs=[gr.File(label="Upload GPS Tracker Log File (CSV format)")],
+    outputs=[
+        gr.Dataframe(label="Intrusion Detection Results"),
+        gr.File(label="Download Predictions CSV")
+    ],
+    title="GPS Tracker Intrusion Detection System",
     description=(
         """
-        Upload a CSV log file with the following features:
-        date,time,door_state,sphone_signal,label
+        Upload a GPS log file with the following features:
+        date,time,latitude,longitude,label,type
         Example:
-        26-04-19,13:59:20,1,-85,normal
+        25-Apr-19,18:31:39,116.521704,132.162504,1,ddos
         """
     )
 )