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Merge pull request 'master' (#12) from master into Zar-Branch

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Reviewed-on: brotoskyj/AirlockTools#12
This commit was merged in pull request #12.
This commit is contained in:
brotoskyj
2025-08-25 11:00:29 -04:00
parent 7b83e6bf16 9886cdf019
commit b85582d6c1
5 changed files with 160 additions and 250 deletions
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AirlockTools.py
+47 -222
View File
@@ -19,8 +19,6 @@ import utils.getdeviceevents
import utils.allowlist
import utils.hashfunctions
import utils.pathfunctions
import utils.allowfunctions
import utils.colortext as ct
import urllib3
import pandas as pd
@@ -66,235 +64,62 @@ def menu_main():
choice = input(ct.colorText("\nEnter Menu Item: ", "white"))
if choice == '1':
utils.getdeviceevents.devicehistory(url,False)
elif choice == "2":
menu_local_approve()
elif choice == "3":
menu_feature2()
elif choice == "4":
menu_prepare_to_enforce()
elif choice == "Q":
break
else:
print(ct.colorText("Invalid choice. Please try again.","red"))
if choice == '2':
utils.allowlist.allowlistexechistories(url,False)
if choice == '3':
executionhist = utils.allowlist.allowlistexechistories(url,True)
print(executionhist)
aggregated = utils.hashfunctions.aggregateHashes(executionhist)
print(aggregated)
augmented = utils.hashfunctions.augmentAggregatedHashes(url,aggregated)
print(augmented)
def menu_local_approve():
while True:
print("\n--- Submenu ---")
print("1. Sub-option A")
print("2. Sub-option B")
print("3. Return to Main Menu")
choice = input("Enter your choice: ")
augmented.to_html("augmentedlist.html", index=False)
if choice == "1":
print("You selected Sub-option A")
elif choice == "2":
print("You selected Sub-option B")
elif choice == "3":
print("Returning to Main Menu...")
break
else:
print("Invalid choice. Please try again.")
badpublisherlist = []
categorized = utils.hashfunctions.categorizeHashes(augmented, 5, badpublisherlist)
categorized[0].to_html("needsreview.html", index=False)
categorized[1].to_html("approved.html", index=False)
categorized[2].to_html("remaining.html", index=False)
def menu_feature2():
while True:
print("\n--- Submenu ---")
print("1. Sub-option A")
print("2. Sub-option B")
print("3. Return to Main Menu")
choice = input("Enter your choice: ")
if choice == '4':
html_file = "augmentedlist.html"
augmented_df = pd.read_html(html_file)
print(augmented_df)
combined_df = pd.concat(augmented_df, ignore_index=True)
if choice == "1":
print("You selected Sub-option A")
elif choice == "2":
print("You selected Sub-option B")
elif choice == "3":
print("Returning to Main Menu...")
break
else:
print("Invalid choice. Please try again.")
def menu_prepare_to_enforce():
first_policy = " "
second_policy = " "
#If the directorys where we're going to store our output dont exist, make them.
if not os.path.exists("dataframe_html"): os.makedirs("dataframe_html")
if not os.path.exists("dataframe_csv"): os.makedirs("dataframe_csv")
if not os.path.exists("approvals"): os.makedirs("approvals")
df_aggregated_combo = pd.DataFrame()
while True:
print(ct.colorText("\n --------------------------------------------------------------------", "cyan"))
print(ct.colorText(" -------------------- Prepare to Enforce Policy ---------------------", "cyan"))
print(ct.colorText(" --------------------------------------------------------------------", "cyan"))
print(ct.colorText("\nSequentually follow these steps to prepare a policy for enforcement:", "white"))
print(ct.colorText("\n1. Choose which policy or policies to work with - : ", "cyan"))
path_eligible, path_ineligible = utils.pathfunctions.filepathInitialGroup(combined_df)
path_eligible.to_html("EligblePaths.html", index=False)
path_ineligible.to_html("IneligiblePaths.html",index=False)
if first_policy == " " and second_policy == " ":
print(ct.colorText(f" [✗] No policies have been chosen","red"))
elif first_policy != " " and second_policy is first_policy:
print(ct.colorText(f" [✓] {first_policy} has been selected,", "green"))
elif first_policy != " " and second_policy != " ":
print(ct.colorText(f" [✓] {first_policy} has been selected as Policy 1","green"))
print(ct.colorText(f" [✓] {second_policy} has been selected as Policy 2","green"))
if choice == '5':
executionhist = utils.allowlist.allowlistexechistories(url,True)
print(executionhist)
print(ct.colorText("2. Pull and stage event history", "cyan"))
aggregated = utils.hashfunctions.aggregateHashes(executionhist)
print(aggregated)
augmented = utils.hashfunctions.augmentAggregatedHashes(url,aggregated)
print(augmented)
augmented.to_html("augmentedlist.html", index=False)
html_file = "augmentedlist.html"
augmented_df = pd.read_html(html_file)
combined_df = pd.concat(augmented_df, ignore_index=True)
path_eligible, path_ineligible = utils.pathfunctions.filepathInitialGroup(combined_df)
path_eligible.to_html("EligblePaths.html", index=False)
path_ineligible.to_html("IneligiblePaths.html",index=False)
if os.path.exists(f"dataframe_csv\\df_aggregated_{first_policy}.csv") == True:
print(ct.colorText(f" [✓] This has been completed for {first_policy}","green"))
elif os.path.exists(f"dataframe_csv\\df_aggregated_{first_policy}.csv") == False:
print(ct.colorText(f" [✗] This step has not been completed","red"))
elif second_policy is not first_policy and os.path.exists(f"dataframe_csv\\df_aggregated_{second_policy}.csv") == True:
print(ct.colorText(f" [✓] This has been completed for {second_policy}","green"))
elif second_policy is not first_policy and os.path.exists(f"dataframe_csv\\df_aggregated_{second_policy}.csv") == False:
print(ct.colorText(f" [✓] This has not been completed for {second_policy}","red"))
print(ct.colorText("3. Combine Staged policies", "cyan"))
if os.path.exists(f"dataframe_csv\\df_aggregated_combo_{first_policy}_{second_policy}.csv") == True:
print(ct.colorText(" [✓] This step has been completed","green"))
else:
print(ct.colorText(" [✗] This step has not been completed","red"))
print(ct.colorText("4. Add hash threat information to list of executions", "cyan"))
badpublisherlist = []
categorized = utils.hashfunctions.categorizeHashes(augmented, 5, badpublisherlist)
categorized[0].to_html("needsreview.html", index=False)
categorized[1].to_html("approved.html", index=False)
categorized[2].to_html("remaining.html", index=False)
if os.path.exists(f"dataframe_csv\\df_augmented_combo_{first_policy}_{second_policy}.csv") == True:
print(ct.colorText(" [✓] This step has been completed","green"))
else:
print(ct.colorText(" [✗] This step has not been completed","red"))
print(ct.colorText("5. Determine if path exclusions are possible", "cyan"))
if os.path.exists(f"dataframe_csv\\df_path_eligible_{first_policy}_{second_policy}.csv") == True:
print(ct.colorText(" [✓] This step has been completed","green"))
else:
print(ct.colorText(" [✗] This step has not been completed", "red"))
print(ct.colorText("6. Categorize your hashes ", "cyan"))
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_unapproved_hashes__{first_policy}_{second_policy}.csv"):
print(ct.colorText(" [✓] This step has been completed","green"))
else:
print(ct.colorText(" [✗] This step has not been completed","red"))
print(ct.colorText("7. Compare potential path exclusions with allowed hashes", "cyan"))
if os.path.exists(f"dataframe_csv\\df_allowed_paths_{first_policy}_{second_policy}.csv") == True:
print(ct.colorText(" [✓] This step has been completed","green"))
else:
print(ct.colorText(" [✗] This step has not been completed","red"))
print(ct.colorText("Q. Quit", "cyan"))
choice = input(ct.colorText("\nEnter your choice: ", "white"))
if choice == "1":
first_policy_tuple = utils.allowlist.listPolicies(url)
first_policy = first_policy_tuple[1][first_policy_tuple[0]]
while True:
answer = input(ct.colorText(f"{"Do you want to load a second policy?"} (yes/no): ", "white").strip().lower())
if answer in ("yes", "y"):
second_policy_tuple = utils.allowlist.listPolicies(url)
second_policy = second_policy_tuple[1][second_policy_tuple[0]]
break
elif answer in ("no", "n"):
second_policy_tuple = first_policy_tuple
second_policy = first_policy
break
else:
print(ct.colorText("Please answer with 'yes' or 'no'.", "red"))
elif choice == "2":
if not os.path.exists("dataframe_csv\\df_aggregated_{first_policy}.csv"):
executionhist_policy1 = utils.allowlist.pullPolicyExechistories(url,first_policy_tuple[0], first_policy_tuple[1],True)
df_aggregated_policy1 = utils.hashfunctions.aggregateHashes(executionhist_policy1)
df_aggregated_policy1.to_html(f"dataframe_html\\df_aggregated_{first_policy}.html", index=False)
df_aggregated_policy1.to_csv(f"dataframe_csv\\df_aggregated_{first_policy}.csv", index=False)
print(ct.colorText(f"Staging of Exection history for policy: {first_policy} is complete","green"))
if not os.path.exists("dataframe_csv\\df_aggregated_{second_policy}.csv"):
executionhist_policy2 = utils.allowlist.pullPolicyExechistories(url,second_policy_tuple[0], second_policy_tuple[1],True)
df_aggregated_policy2 = utils.hashfunctions.aggregateHashes(executionhist_policy2)
df_aggregated_policy2.to_html(f"dataframe_html\\df_aggregated_{second_policy}.html", index=False)
df_aggregated_policy2.to_csv(f"dataframe_csv\\df_aggregated_{second_policy}.csv", index=False)
print(ct.colorText(f"Staging of Exection history for policy: {second_policy} is complete","green"))
elif choice == "3":
if second_policy is first_policy and os.path.exists(f"dataframe_csv\\df_aggregated_{first_policy}.csv"):
df1 = tryToReadCSV(f"dataframe_csv\\df_aggregated_{first_policy}.csv")
df_aggregated_combo = df1
df_aggregated_combo.to_html(f"dataframe_html\\df_aggregated_combo_{first_policy}_{second_policy}.html", index=False)
df_aggregated_combo.to_csv(f"dataframe_csv\\df_aggregated_combo_{first_policy}_{second_policy}.csv", index=False)
elif os.path.exists(f"dataframe_csv\\df_aggregated_{first_policy}.csv") and os.path.exists(f"dataframe_csv\\df_aggregated_{second_policy}.csv"):
df1 = tryToReadCSV(f"dataframe_csv\\df_aggregated_{first_policy}.csv")
df2 = tryToReadCSV(f"dataframe_csv\\df_aggregated_{second_policy}.csv")
df_aggregated_combo = pd.concat([df1 , df2], ignore_index=True)
df_aggregated_combo.to_html(f"dataframe_html\\df_aggregated_combo_{first_policy}_{second_policy}.html", index=False)
df_aggregated_combo.to_csv(f"dataframe_csv\\df_aggregated_combo_{first_policy}_{second_policy}.csv", index=False)
else:
print(ct.colorText(f"Please stage your data before attempting this step","red"))
elif choice == "4":
if os.path.exists(f"dataframe_csv\\df_aggregated_combo_{first_policy}_{second_policy}.csv"):
df_augmented = utils.hashfunctions.augmentAggregatedHashes(url,tryToReadCSV(f"dataframe_csv\\df_aggregated_combo_{first_policy}_{second_policy}.csv"))
df_augmented.to_html(f"dataframe_html\\df_augmented_combo_{first_policy}_{second_policy}.html", index=False)
df_augmented.to_csv(f"dataframe_csv\\df_augmented_combo_{first_policy}_{second_policy}.csv", index=False)
print(ct.colorText(f"Hash reputation info added to dataframe","green"))
else:
print(ct.colorText(f"Please combine your data with step 3 prior to attempting this step","red"))
elif choice == "5":
if os.path.exists(f"dataframe_html\\df_augmented_combo_{first_policy}_{second_policy}.html"):
path_eligible, path_ineligible = utils.pathfunctions.filepathInitialGroup(pd.read_csv(f"dataframe_csv\\df_augmented_combo_{first_policy}_{second_policy}.csv"))
path_eligible.to_html(f"dataframe_html\\df_path_eligible_{first_policy}_{second_policy}.html", index=False)
path_eligible.to_csv(f"dataframe_csv\\df_path_eligible_{first_policy}_{second_policy}.csv", index=False)
path_ineligible.to_html(f"dataframe_html\\df_path_ineligible_{first_policy}_{second_policy}.html", index=False)
path_ineligible.to_csv(f"dataframe_csv\\df_path_ineligible_{first_policy}_{second_policy}.csv", index=False)
print(ct.colorText(f"Eligible paths determined","green"))
else:
print(ct.colorText(f"Please Augment your data with hash threat info using step 4 prior to attempting this step","red"))
elif choice == "6":
if os.path.exists(f"dataframe_csv\\df_augmented_combo_{first_policy}_{second_policy}.csv"):
categorized = utils.hashfunctions.categorizeHashes(pd.read_csv(f"dataframe_csv\\df_augmented_combo_{first_policy}_{second_policy}.csv"), threat_tolerance_constant, badpublisherlist)
categorized[0].to_html(f"dataframe_html\\df_hashes_needing_approval_{first_policy}_{second_policy}.html", index=False)
categorized[0].to_csv(f"dataframe_csv\\df_hashes_needing_approval_{first_policy}_{second_policy}.csv", index=False)
categorized[1].to_html(f"dataframe_html\\df_automatically_approved_hashes_{first_policy}_{second_policy}.html", index=False)
categorized[1].to_csv(f"dataframe_csv\\df_automatically_approved_hashes_{first_policy}_{second_policy}.csv", index=False)
categorized[2].to_html(f"dataframe_html\\df_unapproved_hashes__{first_policy}_{second_policy}.html", index=False)
categorized[2].to_csv(f"dataframe_csv\\df_unapproved_hashes__{first_policy}_{second_policy}.csv", index=False)
print(ct.colorText(f"Hashes have been categorized","green"))
else:
print(ct.colorText(f"Please Augment your data with hash threat info using step 4 prior to attempting this step","red"))
elif choice == "7":
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_unapproved_hashes__{first_policy}_{second_policy}.csv"):
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)
allowpaths.to_html(f"dataframe_html\\df_allowed_paths_{first_policy}_{second_policy}.html", index=False)
allowpaths.to_csv(f"dataframe_csv\\df_allowed_paths_{first_policy}_{second_policy}.csv", index=False)
print(ct.colorText(f"Allowable paths determined","green"))
else:
print(ct.colorText(f"Please complete step 6 prior to attempting this step","red"))
elif choice == "Q":
break
else:
print(ct.colorText("Invalid choice. Please try again.", "red"))
def tryToReadCSV(csv):
try:
df =pd.read_csv(csv)
if df.empty:
print(ct.colorText("Error: CSV file has headers but no data rows.", "red"))
else:
print(ct.colorText("Data loaded successfully.", "green"))
except pd.errors.EmptyDataError:
print(ct.colorText("Notice : CSV file is completely empty (no headers, no data), falling back to empty frame", "white"))
df = pd.DataFrame() # Create an empty DataFrame as fallback
return df
if __name__ == "__main__":
hashtest.py
+5
View File
@@ -0,0 +1,5 @@
hashes = ''
while True:
inputhash = input("Hash: ")
hashes = hashes + ',' + inputhash
print(hashes)
utils/allowlist.py
+68 -2
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@@ -17,7 +17,6 @@ import requests
import json
import os
import time
import utils.colortext as ct
def pullPolicyExechistories(url, choice, policiesnames, outputjson: bool):
@@ -73,4 +72,71 @@ def listPolicies(url):
policyids.append(list['groupid'])
choice = input(ct.colorText("Select Policy Group: ", "white"))
choice = int(choice) - 1
return choice, policiesnames
checkpoint = '000000000000000000000000'
json_output = {'error': 'Success', 'response': {'exechistories': []}}
while True:
json_response_data = checkpoint_stomper(checkpoint, url, policiesnames[choice], headers)
if not json_response_data['response']['exechistories']:
break
for index, item in enumerate(json_response_data['response']['exechistories']):
if index == len(json_response_data['response']['exechistories']) -1:
checkpoint = item['checkpoint']
print(f"Date Greater than 30 Days, Stepping to new Checkpoint. {item['checkpoint']}")
else:
if (datetime.date.today() - datetime.timedelta(days=10) > datetime.datetime.strptime(item['datetime'].replace(' +0000 UTC', ''), '%Y-%m-%dT%H:%M:%SZ').date()):
pass
else:
#json_output['response']['exechistories'].append(json_response_data['response']['exechistories'][1])
for output in json_response_data['response']['exechistories']:
json_output['response']['exechistories'].append(output)
json_output = json.dumps(json_output)
if outputjson == True:
return json_output
#endpoint = url + '/v1/logging/exechistories'
#payload_dict = {
# "type":[1, 2, 6, 7],
# "checkpoint":"000000000000000000000000",
# "policy": [policiesnames[choice]]
#}
#payload = json.dumps(payload_dict)
#print(payload)
#response = requests.request("POST", endpoint, headers=headers, data=payload, verify=False)
#parse_text = json.loads(response.text)
#text_response = checkpoint_stomper(parse_text['response']['exechistories'], url, policiesnames[choice])
#
#if outputjson == False:
# return response
#
#parse_text = json.loads(response.text)
#
#for item in parse_text['response']['exechistories']:
# print(item['checkpoint'])
# print(item['datetime'])
# print(item['hostname'])
# print(item['filename'])
# checkpoint_stomper(item['checkpoint'], endpoint, headers, policiesnames[choice])
def checkpoint_stomper(checkpoint, url, policy, headers):
endpoint = url + '/v1/logging/exechistories'
payload_dict = {
"type":[1,2,6,7],
"checkpoint": checkpoint,
"policy": [policy]
}
payload = json.dumps(payload_dict)
response = requests.request("POST", endpoint, headers=headers, data=payload, verify=False)
parse_text = json.loads(response.text)
return parse_text
#for index, item in enumerate(parse_text):
# if index == len(parse_text) - 1:
# checkpoint = item['checkpoint']
# print(f"Time: {item['datetime']} Checkpoint: {item['checkpoint']}")
# response_fuzzer(checkpoint, url, policyname)
# else:
# if (datetime.date.today() - datetime.timedelta(days=30) > datetime.datetime.strptime(item['datetime'].replace( ' +0000 UTC', ''), '%Y-%m-%dT%H:%M:%SZ').date()):
# pass
# else:
# response_fuzzer(checkpoint, url, policyname)
print("Finished")
utils/hashfunctions.py
+13 -23
View File
@@ -90,38 +90,28 @@ def categorizeHashes(aug_df: pd.DataFrame, threat_tolerance: int, untrusted_publ
df = aug_df.copy()
def reputationtool(row):
val = row["reputation_scannermatch"]
if pd.isna(val) or val == "N/A":
return row["publisher_y"] == "Not Signed"
def reputationtool(row, threat_tolerance):
if row["reputation_scannermatch"] == "N/A":
return True
try:
return int(val) > threat_tolerance
except (ValueError, TypeError):
return row["publisher_y"] == "Not Signed"
df["reputation_flag"] = df.apply(reputationtool, axis=1)
mask_needsreview = (
((df["publisher_y"] == "Not Signed") & df["reputation_flag"]) |
(df["reputation_status"] == "UNKNOWN")
)
mask_needsreview = (df["publisher_y"] == "Not Signed") & df.apply(lambda row: reputationtool(row, threat_tolerance), axis=1)
mask_approved = (
(
(df["publisher_y"] != "Not Signed") &
~df["publisher_y"].isin(untrusted_publishers) &
~df["reputation_status"].isna()
) |
(
(df["publisher_y"] == "Not Signed") &
~df["reputation_flag"] &
~df["publisher_y"].isin(untrusted_publishers) &
~df["reputation_status"].isna()
)
((df["publisher_y"] != "Not Signed") & (~df["publisher_y"].isin(untrusted_publishers))) &
(df["publisher_y"] != "Not Signed") # explicitly signed
) | (
(df["publisher_y"] == "Not Signed") &
(~df.apply(lambda row: reputationtool(row, threat_tolerance), axis=1)) &
(~df["publisher_y"].isin(untrusted_publishers)) # exclude untrusted even if unsigned
)
needsreview_df = df[mask_needsreview]
approved_df = df[mask_approved]
unapproved_df = df[~(mask_needsreview | mask_approved)]
remaining_df = df[~(mask_needsreview | mask_approved)]
return needsreview_df, approved_df, unapproved_df
return needsreview_df, approved_df, remaining_df
utils/pathfunctions.py
+27 -3
View File
@@ -57,21 +57,45 @@ def filepathInitialGroup(df: pd.DataFrame):
break
return join_parts(prefix)
# Step 6: Group directories by shared prefix
# Step 6: Group directories by shared prefix using custom logic
"""
Loop through each directory path
directories: list of all directory paths.
groups: will hold lists of grouped directories.
used: tracks which directories have already been grouped.
"""
directories = df["directory"].tolist()
groups = []
used = set()
#For Each directory, compare it with others
"""
Skip if already grouped.
Start a new group with the current path.
parts_i is the list of folder names in the path (e.g., ["C:", "Users", "John", "Documents"]).
"""
for i, path in enumerate(directories):
if path in used:
continue
group = [path]
parts_i = get_parts(path)
#Compare with all other directories: For each other directory, split it into parts and find the common prefix (shared folder structure).
"""
Logic:
If the directory is deep (>3 parts) and shares at least 3 parts → group it.
If it's exactly 3 parts long and shares at least 2 → group it.
Or, if it shares all but one part and is deep → group it.
These rules are designed to:
Group directories that are closely related in structure.
Avoid grouping unrelated paths that just happen to start similarly.
"""
for j in range(i + 1, len(directories)):
parts_j = get_parts(directories[j])
common = os.path.commonprefix([parts_i, parts_j])
#Apply grouping rules
if (len(parts_i) > 3 and len(common) >= 3) or (len(parts_i) == 3 and len(common) >= 2):
group.append(directories[j])
used.add(directories[j])
@@ -99,7 +123,7 @@ def filepathInitialGroup(df: pd.DataFrame):
path_eligible = grouped_df[grouped_df["depth"] > 2].drop(columns=["depth"])
path_ineligible = grouped_df[grouped_df["depth"] <= 2].drop(columns=["depth"])
# Step 10: Move entries from eligible to ineligible if grouped_directory contains excluded directories
# Step 10: Move entries from eligible to ineligible if grouped_directory contains 'C:\Users' or 'c$\Users'
mask = path_eligible["grouped_directory"].str.contains(r"(?i)(?:\\Users|\\c\$\\Users|inetpub\\wwwroot|windows\\temp)", na=False)
move_to_ineligible = path_eligible[mask]
path_eligible = path_eligible[~mask]