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rl_train.py
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rl_train.py
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import warnings
warnings.filterwarnings("ignore")
import logging
from logging import basicConfig, exception, debug, error, info, warning, getLogger
import argparse
import numpy as np
from itertools import count
from pathlib import Path
from tqdm import tqdm
from datetime import date
import os
from rich.logging import RichHandler
from rich.progress import Progress, TaskID, track
from rich.traceback import install
from rich import print
from rich.panel import Panel
from rich.text import Text
from rich.table import Table
from pyfiglet import Figlet
from collections import namedtuple, deque
from statistics import mean
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.distributions import Categorical
import gym
import gym_malware
from gym_malware.envs.utils import interface, pefeatures
from gym_malware.envs.controls import manipulate2 as manipulate
ACTION_LOOKUP = {i: act for i, act in enumerate(
manipulate.ACTION_TABLE.keys())}
def put_banner():
# Printing heading banner
f = Figlet(font="banner4")
grid = Table.grid(expand=True, padding=1, pad_edge=True)
grid.add_column(justify="right", ratio=38)
grid.add_column(justify="left", ratio=62)
grid.add_row(
Text.assemble((f.renderText("PE"), "bold red")),
Text(f.renderText("Sidious"), "bold white"),
)
print(grid)
print(
Panel(
Text.assemble(
("Creating Chaos with Mutated Evasive Malware with ", "grey"),
("Reinforcement Learning ", "bold red"),
("and "),
("Generative Adversarial Networks", "bold red"),
justify="center",
)
)
)
put_banner()
def parse_args():
parser = argparse.ArgumentParser(description='Reinforcement Training Module')
parser.add_argument('--rl_gamma', type=float, default=0.99, metavar='G',
help='discount factor (default: 0.99)')
parser.add_argument('--seed', type=int, default=543, metavar='N',
help='random seed (default: 543)')
parser.add_argument('--rl_episodes', type=float, default=30000,
help='number of episodes to execute (default: 30000)')
parser.add_argument('--rl_mutations', type=float, default=80,
help='number of maximum mutations allowed (default: 80)')
parser.add_argument('--rl_save_model_interval', type=float, default=500,
help='Interval at which models should be saved (default: 500)') #gitul
parser.add_argument('--rl_output_directory', type= Path, default=Path("rl_models"),
help='number of episodes to execute (default: rl_models/)') #gitul
parser.add_argument("--logfile", help = "The file path to store the logs. (default : rl_features_logs_" + str(date.today()) + ".log)", type = Path, default = Path("rl_features_logs_" + str(date.today()) + ".log"))
logging_level = ["debug", "info", "warning", "error", "critical"]
parser.add_argument(
"-l",
"--log",
dest="log",
metavar="LOGGING_LEVEL",
choices=logging_level,
default="info",
help=f"Select the logging level. Keep in mind increasing verbosity might affect performance. Available choices include : {logging_level}",
)
args = parser.parse_args()
return args
def logging_setup(logfile: str , log_level: str):
from imp import reload
reload(logging)
log_dir = "Logs"
if not os.path.exists(log_dir):
os.mkdir(log_dir)
logfile = os.path.join(log_dir, logfile)
basicConfig(
level=log_level.upper(),
filemode='a', # other options are w for write.
format="%(message)s",
filename=logfile
)
getLogger().addHandler(RichHandler())
info("[*] Starting Reinforcement Learning Agent's Training ...\n")
args = parse_args()
logging_setup(str(args.logfile), args.log)
class Policy(nn.Module):
def __init__(self, env):
super(Policy, self).__init__()
self.layers = nn.Sequential(
nn.Dropout(0.1),
nn.Linear(env.observation_space.shape[0], 1024),
nn.BatchNorm1d(1024),
nn.ELU(alpha=1.0),
nn.Linear(1024, 256),
nn.BatchNorm1d(256),
nn.ELU(alpha=1.0),
nn.Linear(256, env.action_space.n)
)
self.saved_log_probs = []
self.rewards = []
def forward(self, x):
action_scores = self.layers(x)
return action_scores
def update_epsilon(n):
epsilon_start = 1.0
epsilon = epsilon_start
epsilon_final = 0.4
epsilon_decay = 1000 # N from the research paper (equation #6)
epsilon = 1.0 - (n/epsilon_decay)
if epsilon <= epsilon_final:
epsilon = epsilon_final
return epsilon
def select_action(observation, epsilon, env, policy):
rand = np.random.random()
if rand < epsilon:
action = np.random.choice(env.action_space.n)
return action
actions = policy.forward(observation)
m = Categorical(actions)
action = m.sample()
policy.saved_log_probs.append(m.log_prob(action))
debug(f"PRinting ACtion [bold green] {action}", extra={"markup":True})
return action.item()
class RangeNormalize(object):
def __init__(self,
min_val,
max_val):
"""
Normalize a tensor between a min and max value
Arguments
---------
min_val : float
lower bound of normalized tensor
max_val : float
upper bound of normalized tensor
"""
self.min_val = min_val
self.max_val = max_val
def __call__(self, *inputs):
outputs = []
for idx, _input in enumerate(inputs):
_min_val = _input.min()
_max_val = _input.max()
a = (self.max_val - self.min_val) / (_max_val - _min_val)
b = self.max_val- a * _max_val
_input = (_input * a ) + b
outputs.append(_input)
return outputs if idx > 1 else outputs[0]
def finish_episode(gamma, policy):
R = 0
policy_loss = []
returns = []
for r in policy.rewards[::-1]:
R = r + gamma * R
returns.insert(0, R)
returns = torch.tensor(returns)
returns = (returns - returns.mean()) / (returns.std() + eps)
for log_prob, R in zip(policy.saved_log_probs, returns):
policy_loss.append(-log_prob * R)
optimizer.zero_grad()
policy_loss = torch.cat(policy_loss).sum()
policy_loss.backward()
optimizer.step()
del policy.rewards[:]
del policy.saved_log_probs[:]
device = torch.device("cpu")
info("[*] Initilializing environment ...\n")
env = gym.make("malware-score-v0")
env.seed(args.seed)
torch.manual_seed(args.seed)
info("[*] Initilializing Neural Network model ...")
policy = Policy(env)
optimizer = optim.Adam(policy.parameters(), lr=1e-2)
eps = np.finfo(np.float32).eps.item()
def main():
info("[*] Starting training ...")
running_reward = 10
rn = RangeNormalize(-0.5,0.5)
D = args.rl_episodes # as mentioned in the research paper (total number of episodes)
T = args.rl_mutations # as mentioned in the paper (total number of mutations that the agent can perform on one file)
n = 0
for i_episode in range(1, D):
try:
state, ep_reward = env.reset(), 0
state_norm = rn(state)
state_norm = torch.from_numpy(state_norm).float().unsqueeze(0).to(device)
epsilon = update_epsilon(i_episode)
for t in range(1, T): # Don't infinite loop while learning
action = select_action(state_norm, epsilon, env, policy)
state, reward, done, _ = env.step(action)
policy.rewards.append(reward)
ep_reward += reward
debug("\t[+] Episode : " + str(i_episode) + " , Mutation # : " + str(t) + " , Mutation : " + str(ACTION_LOOKUP[action]) + " , Reward : " + str(reward))
if done:
break
debug('\t[+] Episode Over')
finish_episode(args.rl_gamma, policy)
print("here epside " + str(i_episode) + " arg " + str(args.rl_save_model_interval))
if i_episode % args.rl_save_model_interval == 0:
if not os.path.exists(args.rl_output_directory):
os.mkdir(args.rl_output_directory)
info("[*] Feature vector directory has been created at : " + args.rl_output_directory)
torch.save(policy.state_dict(), os.path.join(args.rl_output_directory, "rl-model-" + str(i_episode) + "-" +str(date.today()) + ".pt" ))
info("[*] Saving model in rl-model/ directory ...")
except Exception as e:
#print("exception " + e)
continue
if __name__ == '__main__':
main()