Sniffer_AI(Garage-door Dataset).py

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()