=
303 lines
16 KiB
Python
303 lines
16 KiB
Python
# Copyright (C) 2025 James Brotosky, Brandon Wickline
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#
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero General Public License as published
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# by the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU Affero General Public License for more details.
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#
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# You should have received a copy of the GNU Affero General Public License
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# along with this program. If not, see <https://www.gnu.org/licenses/>.
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import dotenv
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import os
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import utils.getdeviceevents
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import utils.allowlist
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import utils.hashfunctions
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import utils.pathfunctions
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import utils.allowfunctions
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import utils.colortext as ct
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import urllib3
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import pandas as pd
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urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
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dotenv.load_dotenv()
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url = "https://172.17.22.240:3129"
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badpublisherlist = ["Brave Software, Inc.", "Zoom Video Communications, Inc."]
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path_exclusion_constant = 3
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threat_tolerance_constant = 4
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def apivalidation():
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print(ct.colorText(r"""
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_____ .__ .__ __ ___________ .__
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/ _ \ |__|______| | ____ ____ | | __ \__ ___/___ ____ | | ______
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/ /_\ \| \_ __ \ | / _ \_/ ___\| |/ / | | / _ \ / _ \| | / ___/
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/ | \ || | \/ |_( <_> ) \___| < | |( <_> | <_> ) |__\___ \
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\____|__ /__||__| |____/\____/ \___ >__|_ \ |____| \____/ \____/|____/____ >
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\/ \/ \/ \/
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""", "cyan"))
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print(ct.colorText("=================================================================================", "cyan"))
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print(ct.colorText("======================== Welcome to the Airlock API Tool ========================", "cyan"))
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print(ct.colorText("=================================================================================", "cyan"))
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match os.getenv('APIKEY'):
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case '':
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print(ct.colorText("Please add your API Key to the .env file", "red"))
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case _:
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menu_main()
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def menu_main():
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while True:
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print(ct.colorText("\n-----------------------------------", "magenta"))
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print(ct.colorText("------------ Main Menu ------------", "magenta"))
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print(ct.colorText("-----------------------------------", "magenta"))
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print(ct.colorText("1. Get All Events for Single Device", "yellow"))
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print(ct.colorText("2. Placeholder for Local Approval", "yellow"))
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print(ct.colorText("3. Placeholder for Another Tool", "yellow"))
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print(ct.colorText("4. Prepare Policy For Enforcement", "yellow"))
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print(ct.colorText("Q. Quit", "yellow"))
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choice = input(ct.colorText("\nEnter Menu Item: ", "white"))
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if choice == '1':
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utils.getdeviceevents.devicehistory(url,False)
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elif choice == "2":
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menu_local_approve()
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elif choice == "3":
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menu_feature2()
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elif choice == "4":
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menu_prepare_to_enforce()
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elif choice == "Q":
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break
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else:
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print(ct.colorText("Invalid choice. Please try again.","red"))
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def menu_local_approve():
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while True:
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print("\n--- Submenu ---")
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print("1. Sub-option A")
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print("2. Sub-option B")
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print("3. Return to Main Menu")
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choice = input("Enter your choice: ")
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if choice == "1":
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print("You selected Sub-option A")
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elif choice == "2":
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print("You selected Sub-option B")
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elif choice == "3":
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print("Returning to Main Menu...")
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break
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else:
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print("Invalid choice. Please try again.")
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def menu_feature2():
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while True:
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print("\n--- Submenu ---")
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print("1. Sub-option A")
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print("2. Sub-option B")
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print("3. Return to Main Menu")
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choice = input("Enter your choice: ")
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if choice == "1":
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print("You selected Sub-option A")
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elif choice == "2":
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print("You selected Sub-option B")
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elif choice == "3":
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print("Returning to Main Menu...")
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break
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else:
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print("Invalid choice. Please try again.")
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def menu_prepare_to_enforce():
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first_policy = " "
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second_policy = " "
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#If the directorys where we're going to store our output dont exist, make them.
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if not os.path.exists("dataframe_html"): os.makedirs("dataframe_html")
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if not os.path.exists("dataframe_csv"): os.makedirs("dataframe_csv")
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if not os.path.exists("approvals"): os.makedirs("approvals")
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df_aggregated_combo = pd.DataFrame()
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while True:
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print(ct.colorText("\n --------------------------------------------------------------------", "cyan"))
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print(ct.colorText(" -------------------- Prepare to Enforce Policy ---------------------", "cyan"))
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print(ct.colorText(" --------------------------------------------------------------------", "cyan"))
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print(ct.colorText("\nSequentually follow these steps to prepare a policy for enforcement:", "white"))
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print(ct.colorText("\n1. Choose which policy or policies to work with - : ", "cyan"))
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if first_policy == " " and second_policy == " ":
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print(ct.colorText(f" [✗] No policies have been chosen","red"))
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elif first_policy != " " and second_policy is first_policy:
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print(ct.colorText(f" [✓] {first_policy} has been selected,", "green"))
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elif first_policy != " " and second_policy != " ":
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print(ct.colorText(f" [✓] {first_policy} has been selected as Policy 1","green"))
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print(ct.colorText(f" [✓] {second_policy} has been selected as Policy 2","green"))
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print(ct.colorText("2. Pull and stage event history", "cyan"))
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if os.path.exists(f"dataframe_csv\\df_aggregated_{first_policy}.csv") == True:
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print(ct.colorText(f" [✓] This has been completed for {first_policy}","green"))
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elif os.path.exists(f"dataframe_csv\\df_aggregated_{first_policy}.csv") == False:
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print(ct.colorText(f" [✗] This step has not been completed","red"))
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elif second_policy is not first_policy and os.path.exists(f"dataframe_csv\\df_aggregated_{second_policy}.csv") == True:
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print(ct.colorText(f" [✓] This has been completed for {second_policy}","green"))
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elif second_policy is not first_policy and os.path.exists(f"dataframe_csv\\df_aggregated_{second_policy}.csv") == False:
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print(ct.colorText(f" [✓] This has not been completed for {second_policy}","red"))
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print(ct.colorText("3. Combine Staged policies", "cyan"))
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if os.path.exists(f"dataframe_csv\\df_aggregated_combo_{first_policy}_{second_policy}.csv") == True:
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print(ct.colorText(" [✓] This step has been completed","green"))
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else:
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print(ct.colorText(" [✗] This step has not been completed","red"))
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print(ct.colorText("4. Add hash threat information to list of executions", "cyan"))
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if os.path.exists(f"dataframe_csv\\df_augmented_combo_{first_policy}_{second_policy}.csv") == True:
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print(ct.colorText(" [✓] This step has been completed","green"))
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else:
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print(ct.colorText(" [✗] This step has not been completed","red"))
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print(ct.colorText("5. Determine if path exclusions are possible", "cyan"))
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if os.path.exists(f"dataframe_csv\\df_path_eligible_{first_policy}_{second_policy}.csv") == True:
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print(ct.colorText(" [✓] This step has been completed","green"))
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else:
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print(ct.colorText(" [✗] This step has not been completed", "red"))
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print(ct.colorText("6. Categorize your hashes ", "cyan"))
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if os.path.isfile(f"dataframe_csv\\df_hashes_needing_approval_{first_policy}_{second_policy}.csv") and os.path.isfile(f"dataframe_csv\\df_automatically_approved_hashes_{first_policy}_{second_policy}.csv") and os.path.isfile(f"dataframe_csv\\df_remaining_hashes__{first_policy}_{second_policy}.csv"):
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print(ct.colorText(" [✓] This step has been completed","green"))
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else:
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print(ct.colorText(" [✗] This step has not been completed","red"))
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print(ct.colorText("7. Compare potential path exclusions with allowed hashes", "cyan"))
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if os.path.exists(f"dataframe_csv\\df_allowed_paths_{first_policy}_{second_policy}.csv") == True:
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print(ct.colorText(" [✓] This step has been completed","green"))
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else:
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print(ct.colorText(" [✗] This step has not been completed","red"))
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print(ct.colorText("Q. Quit", "cyan"))
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choice = input(ct.colorText("\nEnter your choice: ", "white"))
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if choice == "1":
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first_policy_tuple = utils.allowlist.listPolicies(url)
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first_policy = first_policy_tuple[1][first_policy_tuple[0]]
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while True:
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answer = input(ct.colorText(f"{"Do you want to load a second policy?"} (yes/no): ", "white").strip().lower())
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if answer in ("yes", "y"):
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second_policy_tuple = utils.allowlist.listPolicies(url)
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second_policy = second_policy_tuple[1][second_policy_tuple[0]]
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break
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elif answer in ("no", "n"):
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second_policy_tuple = first_policy_tuple
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second_policy = first_policy
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break
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else:
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print(ct.colorText("Please answer with 'yes' or 'no'.", "red"))
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elif choice == "2":
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if not os.path.exists("dataframe_csv\\df_aggregated_{first_policy}.csv"):
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executionhist_policy1 = utils.allowlist.pullPolicyExechistories(url,first_policy_tuple[0], first_policy_tuple[1],True)
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df_aggregated_policy1 = utils.hashfunctions.aggregateHashes(executionhist_policy1)
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df_aggregated_policy1.to_html(f"dataframe_html\\df_aggregated_{first_policy}.html", index=False)
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df_aggregated_policy1.to_csv(f"dataframe_csv\\df_aggregated_{first_policy}.csv", index=False)
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print(ct.colorText(f"Staging of Exection history for policy: {first_policy} is complete","green"))
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if not os.path.exists("dataframe_csv\\df_aggregated_{second_policy}.csv"):
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executionhist_policy2 = utils.allowlist.pullPolicyExechistories(url,second_policy_tuple[0], second_policy_tuple[1],True)
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df_aggregated_policy2 = utils.hashfunctions.aggregateHashes(executionhist_policy2)
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df_aggregated_policy2.to_html(f"dataframe_html\\df_aggregated_{second_policy}.html", index=False)
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df_aggregated_policy2.to_csv(f"dataframe_csv\\df_aggregated_{second_policy}.csv", index=False)
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print(ct.colorText(f"Staging of Exection history for policy: {second_policy} is complete","green"))
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elif choice == "3":
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if second_policy is first_policy and os.path.exists(f"dataframe_csv\\df_aggregated_{first_policy}.csv"):
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df1 = tryToReadCSV(f"dataframe_csv\\df_aggregated_{first_policy}.csv")
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df_aggregated_combo = df1
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df_aggregated_combo.to_html(f"dataframe_html\\df_aggregated_combo_{first_policy}_{second_policy}.html", index=False)
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df_aggregated_combo.to_csv(f"dataframe_csv\\df_aggregated_combo_{first_policy}_{second_policy}.csv", index=False)
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elif os.path.exists(f"dataframe_csv\\df_aggregated_{first_policy}.csv") and os.path.exists(f"dataframe_csv\\df_aggregated_{second_policy}.csv"):
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df1 = tryToReadCSV(f"dataframe_csv\\df_aggregated_{first_policy}.csv")
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df2 = tryToReadCSV(f"dataframe_csv\\df_aggregated_{second_policy}.csv")
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df_aggregated_combo = pd.concat([df1 , df2], ignore_index=True)
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df_aggregated_combo.to_html(f"dataframe_html\\df_aggregated_combo_{first_policy}_{second_policy}.html", index=False)
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df_aggregated_combo.to_csv(f"dataframe_csv\\df_aggregated_combo_{first_policy}_{second_policy}.csv", index=False)
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else:
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print(ct.colorText(f"Please stage your data before attempting this step","red"))
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elif choice == "4":
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if os.path.exists(f"dataframe_csv\\df_aggregated_combo_{first_policy}_{second_policy}.csv"):
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df_augmented = utils.hashfunctions.augmentAggregatedHashes(url,tryToReadCSV(f"dataframe_csv\\df_aggregated_combo_{first_policy}_{second_policy}.csv"))
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df_augmented.to_html(f"dataframe_html\\df_augmented_combo_{first_policy}_{second_policy}.html", index=False)
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df_augmented.to_csv(f"dataframe_csv\\df_augmented_combo_{first_policy}_{second_policy}.csv", index=False)
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print(ct.colorText(f"Hash reputation info added to dataframe","green"))
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else:
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print(ct.colorText(f"Please combine your data with step 3 prior to attempting this step","red"))
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elif choice == "5":
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if os.path.exists(f"dataframe_html\\df_augmented_combo_{first_policy}_{second_policy}.html"):
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path_eligible, path_ineligible = utils.pathfunctions.filepathInitialGroup(pd.read_csv(f"dataframe_csv\\df_augmented_combo_{first_policy}_{second_policy}.csv"))
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path_eligible.to_html(f"dataframe_html\\df_path_eligible_{first_policy}_{second_policy}.html", index=False)
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path_eligible.to_csv(f"dataframe_csv\\df_path_eligible_{first_policy}_{second_policy}.csv", index=False)
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path_ineligible.to_html(f"dataframe_html\\df_path_ineligible_{first_policy}_{second_policy}.html", index=False)
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path_ineligible.to_csv(f"dataframe_csv\\df_path_ineligible_{first_policy}_{second_policy}.csv", index=False)
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print(ct.colorText(f"Eligible paths determined","green"))
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else:
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print(ct.colorText(f"Please Augment your data with hash threat info using step 4 prior to attempting this step","red"))
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elif choice == "6":
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if os.path.exists(f"dataframe_csv\\df_augmented_combo_{first_policy}_{second_policy}.csv"):
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categorized = utils.hashfunctions.categorizeHashes(pd.read_csv(f"dataframe_csv\\df_augmented_combo_{first_policy}_{second_policy}.csv"), threat_tolerance_constant, badpublisherlist)
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categorized[0].to_html(f"dataframe_html\\df_hashes_needing_approval_{first_policy}_{second_policy}.html", index=False)
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categorized[0].to_csv(f"dataframe_csv\\df_hashes_needing_approval_{first_policy}_{second_policy}.csv", index=False)
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categorized[1].to_html(f"dataframe_html\\df_automatically_approved_hashes_{first_policy}_{second_policy}.html", index=False)
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categorized[1].to_csv(f"dataframe_csv\\df_automatically_approved_hashes_{first_policy}_{second_policy}.csv", index=False)
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categorized[2].to_html(f"dataframe_html\\df_remaining_hashes__{first_policy}_{second_policy}.html", index=False)
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categorized[2].to_csv(f"dataframe_csv\\df_remaining_hashes__{first_policy}_{second_policy}.csv", index=False)
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print(ct.colorText(f"Hashes have been categorized","green"))
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else:
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print(ct.colorText(f"Please Augment your data with hash threat info using step 4 prior to attempting this step","red"))
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elif choice == "7":
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if os.path.exists(f"dataframe_csv\\df_hashes_needing_approval_{first_policy}_{second_policy}.csv") and os.path.exists(f"dataframe_csv\\df_automatically_approved_hashes_{first_policy}_{second_policy}.csv") and os.path.exists(f"dataframe_csv\\df_remaining_hashes__{first_policy}_{second_policy}.csv"):
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allowpaths = utils.allowfunctions.filter_and_drop(pd.read_csv(f"dataframe_csv\\df_automatically_approved_hashes_{first_policy}_{second_policy}.csv"),tryToReadCSV(f"dataframe_csv\\df_path_eligible_{first_policy}_{second_policy}.csv"), path_exclusion_constant)
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allowpaths.to_html(f"dataframe_html\\df_allowed_paths_{first_policy}_{second_policy}.html", index=False)
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allowpaths.to_csv(f"dataframe_csv\\df_allowed_paths_{first_policy}_{second_policy}.csv", index=False)
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print(ct.colorText(f"Allowable paths determined","green"))
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else:
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print(ct.colorText(f"Please complete step 6 prior to attempting this step","red"))
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elif choice == "Q":
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break
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else:
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print(ct.colorText("Invalid choice. Please try again.", "red"))
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def tryToReadCSV(csv):
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try:
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df =pd.read_csv(csv)
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if df.empty:
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print(ct.colorText("Error: CSV file has headers but no data rows.", "red"))
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else:
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print(ct.colorText("Data loaded successfully.", "green"))
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except pd.errors.EmptyDataError:
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print(ct.colorText("Notice : CSV file is completely empty (no headers, no data), falling back to empty frame", "white"))
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df = pd.DataFrame() # Create an empty DataFrame as fallback
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return df
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if __name__ == "__main__":
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apivalidation()
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