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Zar-Branch #17

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mysticmomba merged 6 commits from Zar-Branch into master 2025-08-26 13:15:34 -04:00
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+300 -187
8 changed files with 300 additions and 187 deletions
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AirlockTools.py
+84 -47
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@@ -19,10 +19,10 @@ import utils.getdeviceevents
import utils.allowlist
import utils.hashfunctions
import utils.pathfunctions
import utils.colortext as ct
import utils.pretty as ct
import urllib3
import pandas as pd
import ast
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
dotenv.load_dotenv()
@@ -117,11 +117,12 @@ 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")
if not os.path.exists("manuallyapproved"): os.makedirs("manuallyapproved")
df_aggregated_combo = pd.DataFrame()
while True:
@@ -164,27 +165,32 @@ def menu_prepare_to_enforce():
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"))
print(ct.colorText("5. 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(f"6. Manually review the files \\dataframe_csv\\df_hashes_needing_approval_{first_policy}_{second_policy}.csv and dataframe_csv\\df_automatically_approved_hashes_{first_policy}_{second_policy}.csv", "cyan"))
print(ct.colorText(" Remove the rows containing hashes you do not approve of, and those you would not approve of without metarules.", "cyan"))
print(ct.colorText(" If metarules need to be created, please make note of them, and remove the row from the csv.", "cyan"))
print(ct.colorText(" When complete, save both csv files to the directory 'manuallyapproved' and choose this option to combine these approved hashes with the automatically approved hashes and generate a list of paths to be reviewed", "cyan"))
if os.path.isfile(f"dataframe_csv\\df_paths_needing_review_{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(f"7. Manually review the file df_paths_needing_review_{first_policy}_{second_policy}.csv", "cyan"))
print(ct.colorText(" Remove the rows containing path exclusions you do not approve of" , "cyan"))
print(ct.colorText(" When complete, save the csv file to the directory 'manuallyapproved' and choose this option to generate the proposed list of changes", "cyan"))
if os.path.isfile(f"manuallyapproved\\df_paths_needing_review_{first_policy}_{second_policy}.csv") and os.path.isfile(f"dataframe_csv\\df_hashdestination_{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("Q. Quit", "cyan"))
@@ -209,80 +215,111 @@ def menu_prepare_to_enforce():
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)
ct.style_dataframe_dark(df_aggregated_policy1, f"dataframe_html\\df_aggregated_{first_policy}.html")
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)
ct.style_dataframe_dark(df_aggregated_policy2, f"dataframe_html\\df_aggregated_{second_policy}.html")
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)
ct.style_dataframe_dark(df_aggregated_combo, f"dataframe_html\\df_aggregated_combo_{first_policy}_{second_policy}.html")
print(ct.colorText(f"Dataframes have been aggregated (combined)","green"))
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)
ct.style_dataframe_dark(df_aggregated_combo, f"dataframe_html\\df_aggregated_combo_{first_policy}_{second_policy}.html")
print(ct.colorText(f"Dataframes have been aggregated (combined)","green"))
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)
ct.style_dataframe_dark(df_augmented, f"dataframe_html\\df_augmented_combo_{first_policy}_{second_policy}.html")
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"))
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_csv(f"dataframe_csv\\df_hashes_needing_approval_{first_policy}_{second_policy}.csv", index=False)
ct.style_dataframe_dark(categorized[0], f"dataframe_html\\df_hashes_needing_approval_{first_policy}_{second_policy}.html")
categorized[1].to_csv(f"dataframe_csv\\df_automatically_approved_hashes_{first_policy}_{second_policy}.csv", index=False)
ct.style_dataframe_dark(categorized[1], f"dataframe_html\\df_automatically_approved_hashes_{first_policy}_{second_policy}.html")
categorized[2].to_csv(f"dataframe_csv\\df_unapproved_hashes__{first_policy}_{second_policy}.csv", index=False)
ct.style_dataframe_dark(categorized[2], f"dataframe_html\\df_unapproved_hashes_{first_policy}_{second_policy}.html")
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 == "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"))
if os.path.exists(f"manuallyapproved\\df_hashes_needing_approval_{first_policy}_{second_policy}.csv") and os.path.exists(f"manuallyapproved\\df_automatically_approved_hashes_{first_policy}_{second_policy}.csv"):
df1 = tryToReadCSV(f"manuallyapproved\\df_hashes_needing_approval_{first_policy}_{second_policy}.csv")
df2 = tryToReadCSV(f"manuallyapproved\\df_automatically_approved_hashes_{first_policy}_{second_policy}.csv")
df_all_approved_hashes = pd.concat([df1 , df2], ignore_index=True)
df_all_approved_hashes.to_csv(f"dataframe_csv\\df_all_approved_hashes_{first_policy}_{second_policy}.csv", index=False)
ct.style_dataframe_dark(df_all_approved_hashes, f"dataframe_html\\df_all_approved_hashes_{first_policy}_{second_policy}.html")
df_paths_needing_review, df_path_ineligible = utils.pathfunctions.filepathInitialGroup(pd.read_csv(f"dataframe_csv\\df_all_approved_hashes_{first_policy}_{second_policy}.csv"))
df_paths_needing_review.to_csv(f"dataframe_csv\\df_paths_needing_review_{first_policy}_{second_policy}.csv", index=False)
ct.style_dataframe_dark(df_paths_needing_review, f"dataframe_html\\df_paths_needing_review_{first_policy}_{second_policy}.html")
df_path_ineligible.to_csv(f"dataframe_csv\\df_path_ineligible_{first_policy}_{second_policy}.csv", index=False)
ct.style_dataframe_dark(df_path_ineligible, f"dataframe_html\\df_path_ineligible_{first_policy}_{second_policy}.html")
print(ct.colorText(f"Eligible paths determined","green"))
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"))
print(ct.colorText(f"Please manually approve hashes prior to this step","red"))
elif choice == "7":
if os.path.exists(f"manuallyapproved\\df_paths_needing_review_{first_policy}_{second_policy}.csv"):
df1 = tryToReadCSV(f"manuallyapproved\\df_paths_needing_review_{first_policy}_{second_policy}.csv")
df2 = tryToReadCSV(f"dataframe_csv\\df_path_ineligible_{first_policy}_{second_policy}.csv")
df3 = tryToReadCSV(f"manuallyapproved\\df_automatically_approved_hashes_{first_policy}_{second_policy}.csv")
df_hashdestination = utils.hashfunctions.destinationbuilder(df2,df3)
df_hashdestination.to_csv("dataframe_csv\\df_hashdestination_{first_policy}_{second_policy}.csv")
ct.style_dataframe_dark(df_hashdestination,f"dataframe_html\\df_hashdestination_{first_policy}_{second_policy}.html")
else:
print(ct.colorText(f"Please manually approve suggested paths prior to 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)
hashtest.py
-5
View File
@@ -1,5 +0,0 @@
hashes = ''
while True:
inputhash = input("Hash: ")
hashes = hashes + ',' + inputhash
print(hashes)
utils/allowlist.py
+35 -80
View File
@@ -17,9 +17,10 @@ import requests
import json
import os
import time
import utils.pretty as ct
import ijson
def pullPolicyExechistories(url, choice, policiesnames, outputjson: bool):
headers = {
"X-APIKey": os.getenv('APIKEY')
}
@@ -29,21 +30,37 @@ def pullPolicyExechistories(url, choice, policiesnames, outputjson: bool):
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(ct.colorText(f"Date Greater than 30 Days, Stepping to new Checkpoint. {item['checkpoint']}", "blue"))
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
match_found = False
array_dividend = round(len(json_response_data['response']['exechistories'])/20)
if array_dividend == 0:
array_dividend == 1
for index, item in enumerate(json_response_data['response']['exechistories'][::array_dividend]):
if (datetime.date.today() - datetime.timedelta(days=30) <= datetime.datetime.strptime(item['datetime'].replace(' +0000 UTC', ''), '%Y-%m-%dT%H:%M:%SZ').date()):
print("Found Date Match")
match_found = True
break
checkpoints_processed = round(len(json_response_data['response']['exechistories'])/array_dividend)
print(ct.colorText(f"{checkpoints_processed} checkpoints from this execution have been processed. Stepping to the subsequent checkpoint. {item['checkpoint']}", "blue"))
checkpoint = item['checkpoint']
if match_found == True:
for index, item in enumerate(json_response_data['response']['exechistories']):
if index == len(json_response_data['response']['exechistories']) -1:
checkpoint = item['checkpoint']
print(ct.colorText(f"All checkpoints from this execution have been processed. Stepping to the subsequent checkpoint. {item['checkpoint']}", "blue"))
break
else:
for output in json_response_data['response']['exechistories']:
json_output['response']['exechistories'].append(output)
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:
print(f"Added: {item['datetime']} | {item['checkpoint']} | {item['filename']} | {item['sha256']}")
json_output['response']['exechistories'].append(item)
match_found = False
json_output = json.dumps(json_output)
if outputjson == True:
return json_output
def checkpoint_stomper(checkpoint, url, policy, headers):
json_output = {'error': 'Success', 'response': {'exechistories': []}}
endpoint = url + '/v1/logging/exechistories'
payload_dict = {
"type":[1,2,6,7],
@@ -51,8 +68,13 @@ def checkpoint_stomper(checkpoint, url, policy, headers):
"policy": [policy]
}
payload = json.dumps(payload_dict)
response = requests.request("POST", endpoint, headers=headers, data=payload, verify=False)
parse_text = json.loads(response.text)
with requests.request("POST", endpoint, headers=headers, data=payload, verify=False, stream=True) as response:
parser = ijson.items(response.raw, 'response.exechistories.item')
for item in parser:
key = (item.get('sha256'), item.get('hostname'))
if key not in json_output:
json_output['response']['exechistories'].append(item)
parse_text = json.loads(json.dumps(json_output))
return parse_text
def listPolicies(url):
@@ -72,71 +94,4 @@ def listPolicies(url):
policyids.append(list['groupid'])
choice = input(ct.colorText("Select Policy Group: ", "white"))
choice = int(choice) - 1
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")
return choice, policiesnames
utils/colortext.py
-14
View File
@@ -1,14 +0,0 @@
def colorText(text: str, color: str) -> str:
colors = {
"red": "\033[91m",
"green": "\033[92m",
"yellow": "\033[93m",
"blue": "\033[94m",
"magenta": "\033[95m",
"cyan": "\033[96m",
"white": "\033[97m",
"reset": "\033[0m"
}
return f"{colors.get(color, colors['reset'])}{text}{colors['reset']}"
utils/getdeviceevents.py
+1 -1
View File
@@ -16,7 +16,7 @@ import datetime
import requests
import json
import os
import utils.colortext as ct
import utils.pretty as ct
def devicehistory(url, outputjson: bool):
endpoint = url + '/v1/getexechistory'
utils/hashfunctions.py
+39 -13
View File
@@ -90,28 +90,54 @@ def categorizeHashes(aug_df: pd.DataFrame, threat_tolerance: int, untrusted_publ
df = aug_df.copy()
def reputationtool(row, threat_tolerance):
if row["reputation_scannermatch"] == "N/A":
return True
def reputationtool(row):
val = row["reputation_scannermatch"]
if pd.isna(val) or val == "N/A":
return row["publisher_y"] == "Not Signed"
try:
return int(val) > threat_tolerance
except (ValueError, TypeError):
return row["publisher_y"] == "Not Signed"
mask_needsreview = (df["publisher_y"] == "Not Signed") & df.apply(lambda row: reputationtool(row, threat_tolerance), axis=1)
df["reputation_flag"] = df.apply(reputationtool, axis=1)
mask_approved = (
((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
mask_needsreview = (
((df["publisher_y"] == "Not Signed") & df["reputation_flag"]) |
(df["reputation_status"] == "UNKNOWN")
)
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()
)
)
needsreview_df = df[mask_needsreview]
approved_df = df[mask_approved]
remaining_df = df[~(mask_needsreview | mask_approved)]
unapproved_df = df[~(mask_needsreview | mask_approved)]
return needsreview_df, approved_df, unapproved_df
def destinationbuilder(df, df2):
# Step 1: Explode the 'sha256' list in df2 to create one row per sha256 value
df_expanded = df.explode('sha256')
# Step 2: Create a new dataframe for the result
df_hashdestination = df_expanded.copy()
# Step 3: Populate the 'Destination Allowlist' column based on comparison with df3
df_hashdestination['Destination Allowlist'] = df_hashdestination['sha256'].apply(
lambda x: 'Parent Policy Baseline' if x in df2['sha256'].values else "Destination Policy Allowlist"
)
# Step 4: Return the new dataframe
return df_hashdestination
return needsreview_df, approved_df, remaining_df
utils/pathfunctions.py
+3 -27
View File
@@ -57,45 +57,21 @@ def filepathInitialGroup(df: pd.DataFrame):
break
return join_parts(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.
"""
# Step 6: Group directories by shared prefix
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])
@@ -123,7 +99,7 @@ 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 'C:\Users' or 'c$\Users'
# Step 10: Move entries from eligible to ineligible if grouped_directory contains excluded directories
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]
utils/pretty.py
+138
View File
@@ -0,0 +1,138 @@
def colorText(text: str, color: str) -> str:
colors = {
"red": "\033[91m",
"green": "\033[92m",
"yellow": "\033[93m",
"blue": "\033[94m",
"magenta": "\033[95m",
"cyan": "\033[96m",
"white": "\033[97m",
"reset": "\033[0m"
}
return f"{colors.get(color, colors['reset'])}{text}{colors['reset']}"
def style_dataframe_dark(df, output_html_path=None, overwrite=True):
from datetime import datetime
# Get current date and filename for subtitle
today = datetime.now().strftime("%d %B %Y") # Changed to "Day Month Year"
filename = output_html_path.replace('.html', '') if output_html_path else "Report"
dark_css = """
<style>
body {
background-color: #000000;
margin: 0;
padding: 0;
font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
color: #f8f8f2;
}
.header {
text-align: center;
margin: 20px auto;
padding: 10px;
border-bottom: 2px solid #ffd700;
max-width: 95%;
}
.header h1 {
color: #ffd700;
margin: 0;
font-size: 32px;
}
.header p {
color: #00bfff;
margin: 5px 0 0 0;
font-size: 18px;
}
.table-container {
overflow-y: scroll;
margin: 0 auto;
width: 95%;
max-height: calc(80vh - 100px);
display: block;
border: 1px solid #3a3a4d;
margin-bottom: 0;
}
table {
border-collapse: collapse;
font-size: 14px;
background-color: #1e1e2f;
color: #f8f8f2;
width: max-content;
}
th, td {
border: 1px solid #3a3a4d;
text-align: left;
padding: 10px;
max-width: 300px;
word-wrap: break-word;
overflow-wrap: break-word;
}
/* First column: no wrap */
td:nth-child(1), th:nth-child(1) {
white-space: nowrap;
max-width: none !important;
word-wrap: normal !important;
}
th {
background-color: #2e2e40;
color: #ffd700;
position: sticky;
top: 0;
z-index: 10;
}
tr:nth-child(even) {
background-color: #262638;
}
tr:hover {
background-color: #33334d;
color: #00bfff;
}
/* Custom scrollbar styling */
.table-container::-webkit-scrollbar {
width: 12px;
}
.table-container::-webkit-scrollbar-track {
background: #1e1e2f;
}
.table-container::-webkit-scrollbar-thumb {
background-color: #3a3a4d;
border-radius: 6px;
}
</style>
"""
header = f"""
<div class="header">
<h1>Airlock Tools</h1>
<p>{filename} - {today}</p>
</div>
"""
html_table = df.to_html(index=False, escape=False)
styled_html = (
f"<html>\n"
f"<head><title>Airlock Tools Report</title></head>\n"
f"<body>\n"
f"{dark_css}\n"
f"{header}\n"
f"<div class='table-container'>\n"
f" {html_table}\n"
f"</div>\n"
f"</body>\n"
f"</html>"
)
if output_html_path:
with open(output_html_path, "w", encoding="utf-8") as f:
f.write(styled_html)
print(f"✅ Styled table saved to '{output_html_path}'")
elif overwrite:
import tempfile
temp_path = tempfile.mktemp(suffix=".html")
with open(temp_path, "w", encoding="utf-8") as f:
f.write(styled_html)
print(f"✅ Styled table saved to temporary file: {temp_path}")
else:
return styled_html