utils.py

import time 
import pandas as pd
import os 
import subprocess

LOG_FILE = 'logs'
RUNTIME_CAPTURE = os.path.join('datasets','runtime','capture.csv')
OS_INSTALLATION_PATHS = [
    # Common Unix/Linux directories
    "/bin",
    "/sbin",
    "/usr/bin",
    "/usr/sbin",
    "/opt",
    "/usr/local/bin",
    "/usr/local/sbin",
    "/snap/bin",
    "/var/lib/flatpak/app",

    # Common Windows installation directories
    "C:\\Program Files",
    "C:\\Program Files (x86)",
    f"C:\\Users\\{os.getlogin()}\\AppData\\Local\\Programs",
    f"C:\\Users\\{os.getlogin()}\\AppData\\Roaming",
    f"C:\\Users\\{os.getlogin()}\\AppData\\Local",
    f"C:\\Users\\{os.getlogin()}\\AppData\\Roaming\\Microsoft\\Windows\\Start Menu\\Programs",
    f"C:\\Users\\{os.getlogin()}\\Desktop",  
    f"C:\\Users\\{os.getlogin()}\\AppData\\Local\\Microsoft\\WinGet\\Packages",
    f"C:\\Users\\{os.getlogin()}\\AppData\Local\Temp",
    f"C:\\Program Files\\WindowsApps"
]


# Utility function for time measurement
def current_ms() -> int:
    return round(time.time() * 1000)

def run_command(command):
    print("RUNNING COMMAND " + command)
    subprocess.call(command.split(" "), stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, shell=False)

def preprocess_dataset(df: pd.DataFrame):
    """
    Preprocess dataset.
    Converts timestamps, categorical features, and binary flags into numerical features.
    Removes source and destination IPs and ports from classification.
    """
    
    df.drop(columns=['start_request_time', 'end_request_time'], inplace=True)
    df.drop(columns=['start_response_time', 'end_response_time'], inplace=True)
    
    df.drop(columns=['src_ip', 'dst_ip', 'src_port', 'dst_port'], inplace=True)
    
    flag_columns = ['SYN', 'ACK', 'FIN', 'RST', 'URG', 'PSH']
    for flag in flag_columns:
        df[flag] = df[flag].astype(int)
    
    df['duration'] = df['duration'].astype(float)
    
    if 'label' in df.columns: # if label exists for training purposes
        df['label'] = df['label'].astype(int)
        
    return df

# Save logs to a file
def save_logs(log_message):
    with open(LOG_FILE, 'a') as log_file:
        for x in log_message:
            log_file.write(x)