def __init__(self, args):
# initial setup of network model variables
super(NetworkModel, self).__init__(args)
self.last_action_scan = False
self.fresh = True
# Collected Data #
self.unique_nodes_scanned = []
self.num_scans = 0
# Network Configuration Data #
self.network_config = json.load(open(config_parser.network_cfg, "r"))
# read in reward values for 4 vulns
self.sql_reward = int(self.network_config['sql_reward'])
self.ftp_reward = int(self.network_config['ftp_reward'])
self.smtp_reward = int(self.network_config['smtp_reward'])
self.vnc_reward = int(self.network_config['vnc_reward'])
self.scan_reward = int(self.network_config['scan_reward'])
self.move_reward = int(self.network_config['move_reward'])
self.exit_reward = int(self.network_config['exit_reward'])
self.illegal_move_penalty = int(self.network_config['illegal_move_penalty'])
self.map_text, _ = config_parser.parse_map(self.network_config['layout_file'])
self.node_list = {}
# read in raw text and build network based on it
state = 0
for item in self.map_text:
if state == 0:
if item == 'Node Type':
state = 1
continue
if state == 1:
if item == 'Adjacency':
state = 2
continue
else:
stuff = item.split(':')
if stuff[0] not in self.node_list.keys():
self.node_list[stuff[0]] = NodeData(stuff[0], [], stuff[1], [])
if state == 2:
if item == 'end':
break
else:
adj = item.split(' ')
for node in self.node_list.values():
if node.name == adj[0]:
node.update_adj_list(adj[1:])
def __init__(self, args):
super(CyberModel, self).__init__(args)
# logging utility
self.logger = logging.getLogger('POMDPy.CyberModel')
self.cyber_config = json.load(open(config_parser.cyber_cfg, "r"))
# -------- Model configurations -------- #
# The reward for sampling a good
self.good_cyber_reward = self.cyber_config['good_cyber_reward']
# The penalty for sampling a bad
self.bad_cyber_penalty = self.cyber_config['bad_cyber_penalty']
# The reward for exiting the map
self.exit_reward = self.cyber_config['exit_reward']
# The penalty for an illegal move.
self.illegal_move_penalty = self.cyber_config['illegal_move_penalty']
# penalty for finishing without sampling a cyber
self.half_efficiency_distance = self.cyber_config[
'half_efficiency_distance']
# ------------- Flags --------------- #
# Flag that checks whether the agent has yet to successfully sample
self.sampled_cyber_yet = False
# Flag that keeps track of whether the agent currently believes there are still good
self.any_good_cybers = False
# ------------- Data Collection ---------- #
self.unique_cybers_sampled = []
self.num_times_sampled = 0.0
self.good_samples = 0.0
self.num_reused_nodes = 0
self.num_bad_cybers_sampled = 0
self.num_good_checks = 0
self.num_bad_checks = 0
# -------------- Map data ---------------- #
# The number of rows in the map.
self.n_rows = 0
# The number of columns in the map
self.n_cols = 0
# The number of cybers on the map.
self.n_cybers = 0
self.num_states = 0
self.min_val = 0
self.max_val = 0
self.start_position = GridPosition()
# The coordinates of the cybers.
self.cyber_positions = []
# The coordinates of the goal squares.
self.goal_positions = []
# The environment map in vector form.
# List of lists of RSCellTypes
self.env_map = []
# The distance from each cell to the nearest goal square.
self.goal_distances = []
# The distance from each cell to each cyber.
self.cyber_distances = []
# Smart cyber data
self.all_cyber_data = []
# Actual cyber states
self.actual_cyber_states = []
# The environment map in text form.
self.map_text, dimensions = config_parser.parse_map(
self.cyber_config['map_file'])
self.n_rows = int(dimensions[0])
self.n_cols = int(dimensions[1])
self.initialize()