mysticmomba/AirlockTools
1
1
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases 3 Wiki Activity

This is working better so far

Browse Source
This commit is contained in:
=
2025-08-26 20:49:03 -04:00
parent b7082ffb5e
commit b3bced32b3
1 changed files with 109 additions and 110 deletions
Download Patch File Download Diff File Expand all files Collapse all files
AirlockTools.py
+109 -110
View File
@@ -22,6 +22,7 @@ import utils.pathfunctions
import utils.pretty as ct import utils.pretty as ct
import urllib3 import urllib3
import pandas as pd import pandas as pd
import json
import ast import ast
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
@@ -52,6 +53,20 @@ def apivalidation():
case _: case _:
menu_main() menu_main()
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(f"Data loaded successfully from {csv}", "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
def menu_main(): def menu_main():
while True: while True:
print(ct.colorText("\n-----------------------------------", "magenta")) print(ct.colorText("\n-----------------------------------", "magenta"))
@@ -141,53 +156,47 @@ def menu_prepare_to_enforce():
print(ct.colorText(f" [✓] {first_policy} has been selected as Policy 1","green")) 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")) print(ct.colorText(f" [✓] {second_policy} has been selected as Policy 2","green"))
print(ct.colorText("2. Pull and stage event history", "cyan")) print(ct.colorText("2. Pulls and stages event history, combines the histories, adds hash info, then categorizes the hashes", "cyan"))
if os.path.exists(f"dataframe_csv\\df_aggregated_{first_policy}.csv") == True: if os.path.exists(f"dataframe_csv\\executionhist_{first_policy}.csv"):
print(ct.colorText(f" [✓] This has been completed for {first_policy}","green")) print(ct.colorText(f" [✓] Execution history has been compiled for {first_policy}","green"))
elif os.path.exists(f"dataframe_csv\\df_aggregated_{first_policy}.csv") == False: elif not os.path.exists(f"dataframe_csv\\executionhist_{first_policy}.csv"):
print(ct.colorText(f" [✗] This step has not been completed","red")) print(ct.colorText(f" [✗] Execution history has not been compiled for {first_policy}","red"))
elif second_policy is not first_policy and os.path.exists(f"dataframe_csv\\df_aggregated_{second_policy}.csv") == True: elif second_policy is not first_policy and os.path.exists(f"dataframe_csv\\executionhist_{second_policy}.csv"):
print(ct.colorText(f" [✓] This has been completed for {second_policy}","green")) print(ct.colorText(f" [✓] Execution history has been compiled for {second_policy}","green"))
elif second_policy is not first_policy and os.path.exists(f"dataframe_csv\\df_aggregated_{second_policy}.csv") == False: elif second_policy is not first_policy and not os.path.exists(f"dataframe_csv\\executionhist_{second_policy}.csv"):
print(ct.colorText(f" [✓] This has not been completed for {second_policy}","red")) print(ct.colorText(f" [✗] Execution history has not been compiled for {second_policy}","red"))
print(ct.colorText("3. Combine Staged policies", "cyan")) if os.path.exists(f"dataframe_csv\\execuctionhist_combined_{first_policy}_{second_policy}.csv"):
print(ct.colorText(f" [✓] Execution history has been_combined_for {first_policy} and_{second_policy}", "green"))
if os.path.exists(f"dataframe_csv\\df_aggregated_combo_{first_policy}_{second_policy}.csv") == True: else:
print(ct.colorText(" [✓] This step has been completed","green")) print(ct.colorText(f" [✗] Execution history has not been_combined_for {first_policy} and_{second_policy}", "red"))
if os.path.exists(f"dataframe_csv\\augmented_combo_{first_policy}_{second_policy}.csv"):
print(ct.colorText(f" [✓] Hash Info has been added to the combined execution history", "green"))
else:
print(ct.colorText(f" [✗] Hash Info has not been added to the combined execution history", "red"))
if os.path.exists(f"dataframe_csv\\hashes_needing_approval_{first_policy}_{second_policy}.csv") and os.path.exists(f"dataframe_csv\\automatically_approved_hashes_{first_policy}_{second_policy}.csv") and os.path.exists(f"dataframe_csv\\unapproved_hashes__{first_policy}_{second_policy}.csv"):
print(ct.colorText(f" [✓] Hashes have been cateogrized", "green"))
else: else:
print(ct.colorText(" [✗] This step has not been completed","red")) print(ct.colorText(f" [✗] Hashes have not been cateogrized", "red"))
print(ct.colorText("4. Add hash threat information to list of executions", "cyan")) print(ct.colorText(f"3. Manually review the files '\\dataframe_csv\\hashes_needing_approval_{first_policy}_{second_policy}.csv' and 'dataframe_csv\\automatically_approved_hashes_{first_policy}_{second_policy}.csv'", "cyan"))
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. 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(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(" 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(" 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")) 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"): if os.path.isfile(f"dataframe_csv\\paths_needing_review_{first_policy}_{second_policy}.csv"):
print(ct.colorText(" [✓] This step has been completed","green")) print(ct.colorText(" [✓] This step has been completed","green"))
else: else:
print(ct.colorText(" [✗] This step has not been completed","red")) 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(f"4. Manually review the file '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(" 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 preflight lists", "cyan")) print(ct.colorText(" When complete, save the csv file to the directory 'manuallyapproved' and choose this option to generate the preflight lists", "cyan"))
if os.path.isfile(f"manuallyapproved\\df_paths_needing_review_{first_policy}_{second_policy}.csv") and os.path.isfile("dataframe_csv\\df_hashdestination_{first_policy}_{second_policy}.csv") and os.path.isfile(f"dataframe_csv\\df_addtochildpolicy_{first_policy}_{second_policy}.csv") and os.path.isfile(f"dataframe_csv\\f_addtobaseline_{first_policy}_{second_policy}.csv"): if os.path.isfile(f"manuallyapproved\\paths_needing_review_{first_policy}_{second_policy}.csv") and os.path.isfile("dataframe_csv\\hashdestination_{first_policy}_{second_policy}.csv") and os.path.isfile(f"dataframe_csv\\addtochildpolicy_{first_policy}_{second_policy}.csv") and os.path.isfile(f"dataframe_csv\\addtobaseline_{first_policy}_{second_policy}.csv"):
print(ct.colorText(" [✓] This step has been completed","green")) print(ct.colorText(" [✓] This step has been completed","green"))
else: else:
print(ct.colorText(" [✗] This step has not been completed","red")) print(ct.colorText(" [✗] This step has not been completed","red"))
@@ -213,83 +222,85 @@ def menu_prepare_to_enforce():
print(ct.colorText("Please answer with 'yes' or 'no'.", "red")) print(ct.colorText("Please answer with 'yes' or 'no'.", "red"))
elif choice == "2": elif choice == "2":
if not os.path.exists("dataframe_csv\\df_aggregated_{first_policy}.csv"): if not os.path.exists(f"dataframe_csv\\executionhist_{first_policy}.csv"):
executionhist_policy1 = utils.allowlist.pullPolicyExechistories(url,first_policy_tuple[0], first_policy_tuple[1],True) exe1 = utils.allowlist.pullPolicyExechistories(url,first_policy_tuple[0], first_policy_tuple[1],True)
df_aggregated_policy1 = utils.hashfunctions.aggregateHashes(executionhist_policy1) data = json.loads(exe1)
df_aggregated_policy1.to_csv(f"dataframe_csv\\df_aggregated_{first_policy}.csv", index=False) executionhist_policy1 = pd.DataFrame(data["response"]["exechistories"])
ct.style_dataframe_dark(df_aggregated_policy1, f"dataframe_html\\df_aggregated_{first_policy}.html") executionhist_policy1.to_csv(f"dataframe_csv\\executionhist_{first_policy}.csv", index=False)
print(ct.colorText(f"Staging of Exection history for policy: {first_policy} is complete","green")) ct.style_dataframe_dark(executionhist_policy1, f"dataframe_html\\executionhist_{first_policy}.html")
print(ct.colorText(f"Staging of Execution history for policy: {first_policy} is complete","green"))
if not os.path.exists("dataframe_csv\\df_aggregated_{second_policy}.csv"): if not os.path.exists(f"dataframe_csv\\executionhist_{second_policy}.csv"):
executionhist_policy2 = utils.allowlist.pullPolicyExechistories(url,second_policy_tuple[0], second_policy_tuple[1],True) exe2 = utils.allowlist.pullPolicyExechistories(url,first_policy_tuple[0], first_policy_tuple[1],True)
df_aggregated_policy2 = utils.hashfunctions.aggregateHashes(executionhist_policy2) data2 = json.loads(exe2)
df_aggregated_policy2.to_csv(f"dataframe_csv\\df_aggregated_{second_policy}.csv", index=False) executionhist_policy2 = pd.DataFrame(data2["response"]["exechistories"])
ct.style_dataframe_dark(df_aggregated_policy2, f"dataframe_html\\df_aggregated_{second_policy}.html") executionhist_policy2.to_csv(f"dataframe_csv\\executionhist_{second_policy}.csv", index=False)
print(ct.colorText(f"Staging of Exection history for policy: {second_policy} is complete","green")) ct.style_dataframe_dark(executionhist_policy2, f"dataframe_html\\executionhist_{second_policy}.html")
print(ct.colorText(f"Staging of Exection history for policy: {first_policy} is complete","green"))
elif choice == "3": #Combine the two policies execution histories
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") if second_policy is first_policy:
df_aggregated_combo = df1 execuctionhist_combined = executionhist_policy1
df_aggregated_combo.to_csv(f"dataframe_csv\\df_aggregated_combo_{first_policy}_{second_policy}.csv", index=False) execuctionhist_combined.to_csv(f"dataframe_csv\\execuctionhist_combined_{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") ct.style_dataframe_dark(df_aggregated_combo, f"dataframe_html\\execuctionhist_combined_{first_policy}_{second_policy}.html")
print(ct.colorText(f"Dataframes have been aggregated (combined)","green")) print(ct.colorText(f"Dataframes have been 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"): elif os.path.exists(f"dataframe_csv\\executionhist_{first_policy}.csv") and os.path.exists(f"dataframe_csv\\executionhist_{second_policy}.csv"):
df1 = tryToReadCSV(f"dataframe_csv\\df_aggregated_{first_policy}.csv") execuctionhist_combined = pd.concat([tryToReadCSV(f"dataframe_csv\\executionhist_{first_policy}.csv") , tryToReadCSV(f"dataframe_csv\\executionhist_{second_policy}.csv")], ignore_index=True)
df2 = tryToReadCSV(f"dataframe_csv\\df_aggregated_{second_policy}.csv") execuctionhist_combined.to_csv(f"dataframe_csv\\execuctionhist_combined_{first_policy}_{second_policy}.csv", index=False)
df_aggregated_combo = pd.concat([df1 , df2], ignore_index=True) ct.style_dataframe_dark(df_aggregated_combo, f"dataframe_html\\execuctionhist_combined_{first_policy}_{second_policy}.html")
df_aggregated_combo.to_csv(f"dataframe_csv\\df_aggregated_combo_{first_policy}_{second_policy}.csv", index=False) print(ct.colorText(f"Dataframes have been combined","green"))
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: #Keep only unique combinations of hash, filename, and hostname
print(ct.colorText(f"Please stage your data before attempting this step","red")) if f"dataframe_csv\\execuctionhist_combined_{first_policy}_{second_policy}.csv":
unique_executions = tryToReadCSV(f"dataframe_csv\\execuctionhist_combined_{first_policy}_{second_policy}.csv").drop_duplicates(subset=['sha256', 'filename', 'hostname'])
unique_executions.to_csv(f"dataframe_csv\\unique_executions{first_policy}_{second_policy}.csv")
ct.style_dataframe_dark(unique_executions, f"dataframe_html\\unique_execuctions.html")
elif choice == "4":
if os.path.exists(f"dataframe_csv\\df_aggregated_combo_{first_policy}_{second_policy}.csv"): #Add Hash info to the combined execution history
df_augmented = utils.hashfunctions.augmentAggregatedHashes(url,tryToReadCSV(f"dataframe_csv\\df_aggregated_combo_{first_policy}_{second_policy}.csv")) if not os.path.exists(f"dataframe_html\\augmented_combo_{first_policy}_{second_policy}.html"):
df_augmented.to_csv(f"dataframe_csv\\df_augmented_combo_{first_policy}_{second_policy}.csv", index=False) print(ct.colorText(f"Preparing to pull hash info","green"))
ct.style_dataframe_dark(df_augmented, f"dataframe_html\\df_augmented_combo_{first_policy}_{second_policy}.html") augmented_combo= utils.hashfunctions.augmentAggregatedHashes(url,tryToReadCSV(f"dataframe_csv\\unique_executions{first_policy}_{second_policy}.csv"))
augmented_combo.to_csv(f"dataframe_csv\\augmented_combo_{first_policy}_{second_policy}.csv", index=False)
ct.style_dataframe_dark(augmented_combo, f"dataframe_html\\augmented_combo_{first_policy}_{second_policy}.html")
print(ct.colorText(f"Hash reputation info added to dataframe","green")) print(ct.colorText(f"Hash reputation info added to dataframe","green"))
#Categorize the hashes
if os.path.exists(f"dataframe_csv\\hashes_needing_approval_{first_policy}_{second_policy}.csv") and os.path.exists(f"dataframe_csv\\automatically_approved_hashes_{first_policy}_{second_policy}.csv") and os.path.exists(f"dataframe_csv\\unapproved_hashes__{first_policy}_{second_policy}.csv"):
break
else: else:
print(ct.colorText(f"Please combine your data with step 3 prior to attempting this step","red")) categorized = utils.hashfunctions.categorizeHashes(pd.read_csv(f"dataframe_csv\\augmented_combo_{first_policy}_{second_policy}.csv"), threat_tolerance_constant, badpublisherlist)
elif choice == "5": categorized[0].to_csv(f"dataframe_csv\\hashes_needing_approval_{first_policy}_{second_policy}.csv", index=False)
if os.path.exists(f"dataframe_csv\\df_augmented_combo_{first_policy}_{second_policy}.csv"): ct.style_dataframe_dark(categorized[0], f"dataframe_html\\dashes_needing_approval_{first_policy}_{second_policy}.html")
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) categorized[1].to_csv(f"dataframe_csv\\automatically_approved_hashes_{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") ct.style_dataframe_dark(categorized[1], f"dataframe_html\\automatically_approved_hashes_{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")
categorized[2].to_csv(f"dataframe_csv\\unapproved_hashes__{first_policy}_{second_policy}.csv", index=False)
ct.style_dataframe_dark(categorized[2], f"dataframe_html\\unapproved_hashes_{first_policy}_{second_policy}.html")
print(ct.colorText(f"Hashes have been categorized","green")) 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 == "3":
if os.path.exists(f"manuallyapproved\\hashes_needing_approval_{first_policy}_{second_policy}.csv") and os.path.exists(f"manuallyapproved\\automatically_approved_hashes_{first_policy}_{second_policy}.csv"):
elif choice == "6": df1 = tryToReadCSV(f"manuallyapproved\\hashes_needing_approval_{first_policy}_{second_policy}.csv")
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"): df2 = tryToReadCSV(f"manuallyapproved\\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 = 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) df_all_approved_hashes.to_csv(f"dataframe_csv\\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") ct.style_dataframe_dark(df_all_approved_hashes, f"dataframe_html\\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, df_path_ineligible = utils.pathfunctions.filepathInitialGroup(pd.read_csv(f"dataframe_csv\\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) df_paths_needing_review.to_csv(f"dataframe_csv\\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") ct.style_dataframe_dark(df_paths_needing_review, f"dataframe_html\\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) df_path_ineligible.to_csv(f"dataframe_csv\\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") ct.style_dataframe_dark(df_path_ineligible, f"dataframe_html\\path_ineligible_{first_policy}_{second_policy}.html")
print(ct.colorText(f"Eligible paths determined","green")) print(ct.colorText(f"Eligible paths determined","green"))
@@ -297,11 +308,11 @@ def menu_prepare_to_enforce():
print(ct.colorText(f"Please manually approve hashes prior to this step","red")) print(ct.colorText(f"Please manually approve hashes prior to this step","red"))
elif choice == "7": elif choice == "4":
if os.path.exists(f"manuallyapproved\\df_paths_needing_review_{first_policy}_{second_policy}.csv"): if os.path.exists(f"manuallyapproved\\paths_needing_review_{first_policy}_{second_policy}.csv"):
df_approved = tryToReadCSV(f"manuallyapproved\\df_paths_needing_review_{first_policy}_{second_policy}.csv") df_approved = tryToReadCSV(f"manuallyapproved\\paths_needing_review_{first_policy}_{second_policy}.csv")
df_eligible = tryToReadCSV(f"dataframe_csv\\df_paths_needing_review_{first_policy}_{second_policy}.csv") df_eligible = tryToReadCSV(f"dataframe_csv\\paths_needing_review_{first_policy}_{second_policy}.csv")
df_ineligible = tryToReadCSV(f"dataframe_csv\\df_path_ineligible_{first_policy}_{second_policy}.csv") df_ineligible = tryToReadCSV(f"dataframe_csv\\path_ineligible_{first_policy}_{second_policy}.csv")
approved_set = set([tuple(map(tuple, row)) for row in df_approved.values]) approved_set = set([tuple(map(tuple, row)) for row in df_approved.values])
@@ -315,11 +326,11 @@ def menu_prepare_to_enforce():
ct.style_dataframe_dark(df_approved, f"preflight\\Approved_Path_Exclusions_{first_policy}_{second_policy}.html") ct.style_dataframe_dark(df_approved, f"preflight\\Approved_Path_Exclusions_{first_policy}_{second_policy}.html")
#Seperate out what we arent excluding by path into those that will go into the baseline, and those that will b added to the child. #Seperate out what we arent excluding by path into those that will go into the baseline, and those that will b added to the child.
df_addtobaseline = df_ineligible[df_ineligible['reputation status'] == 'KNOWN'] df_addtobaseline = df_ineligible[df_ineligible['reputation_status'] == 'KNOWN']
df_addtobaseline.to_csv(f"preflight\\Add_to_Baseline_{first_policy}_{second_policy}.csv", index=False) df_addtobaseline.to_csv(f"preflight\\Add_to_Baseline_{first_policy}_{second_policy}.csv", index=False)
ct.style_dataframe_dark(df_addtobaseline, f"preflight\\Add_to_Baseline_{first_policy}_{second_policy}.html") ct.style_dataframe_dark(df_addtobaseline, f"preflight\\Add_to_Baseline_{first_policy}_{second_policy}.html")
df_addtochildpolicy = df_ineligible[df_ineligible['reputation status'] == 'UNKNOWN'] df_addtochildpolicy = df_ineligible[df_ineligible['reputation_status'] == 'UNKNOWN']
df_addtochildpolicy.to_csv(f"preflight\\Add_to_Child_Policy_{first_policy}_{second_policy}.csv", index=False) df_addtochildpolicy.to_csv(f"preflight\\Add_to_Child_Policy_{first_policy}_{second_policy}.csv", index=False)
ct.style_dataframe_dark(df_addtochildpolicy, f"preflight\\Add_to_Child_Policy_{first_policy}_{second_policy}.html") ct.style_dataframe_dark(df_addtochildpolicy, f"preflight\\Add_to_Child_Policy_{first_policy}_{second_policy}.html")
@@ -338,18 +349,6 @@ def menu_prepare_to_enforce():
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__": if __name__ == "__main__":