Paths fixed... need to double check the path exclusion format to feed api
This commit is contained in:
=
+66
-109
@@ -18,6 +18,7 @@ import gc
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import json
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import os
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import pandas as pd
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import re
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import urllib3
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import utils.allowlist
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import utils.getdeviceevents
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@@ -26,13 +27,14 @@ import utils.pathfunctions
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import utils.policyfunctions
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import utils.pretty as ct
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urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
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dotenv.load_dotenv()
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#Constants
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url = os.getenv('url')
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badpublisherlist = ["Brave Software, Inc.", "Zoom Video Communications, Inc.", "GlavSoft LLC"]
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bad_publisher_list = ["Brave","Zoom", "GlavSoft", "VNC"]
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pups = ["logmein", "invalid"]
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badpathparts = ["users", "wwwroot", "windows\\temp", "windows\\task", "windows\\system32", "startup", "windows\\fonts", "Recycle.Bin", "AppData", "programdata"]
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path_exclusion_constant = 3
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@@ -277,7 +279,7 @@ def menu_prepare_to_enforce():
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categorized = utils.hashfunctions.categorizeHashes(
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pd.read_parquet(f"parquet\\combined_hashlist_{first_policy}_{second_policy}.parquet"),
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threat_tolerance_constant,
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badpublisherlist,
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bad_publisher_list,
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pups
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)
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@@ -325,86 +327,15 @@ def menu_prepare_to_enforce():
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del condensed_combo
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gc.collect()
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if not os.path.exists(f"needs_approved\\hashes_rep_unknown_{first_policy}_{second_policy}.csv"):
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condensed_combo = pd.read_parquet(f"parquet\\condensed_executions_{first_policy}_{second_policy}.parquet")
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needsapproval= pd.read_parquet(f"parquet\\hashes_rep_unknown_{first_policy}_{second_policy}.parquet")
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#Pull hash info for the entries in the needs approval table
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needsapproval = pd.merge(condensed_combo, needsapproval, on='sha256', how='inner')
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#Deduplicate lists in the columns
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for col in needsapproval.columns:
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if needsapproval[col].apply(lambda x: isinstance(x, list)).all():
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needsapproval[col] = needsapproval[col].apply(deduplicate_list)
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#Rename Publisher, Keep and reorder columns we want
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needsapproval = needsapproval.rename(columns={'publisher_x': 'publisher'})
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needsapproval = needsapproval[['sha256', 'publisher', 'description', 'filename', 'hostname', 'username', 'productname', 'productversion','reputation_lastseen', 'reputation_scannermatch', 'reputation_scannercount','reputation_status', 'reputation_threatlevel', 'reputation_threatname','reputation_timestamp', 'pprocess', 'gprocess', 'commandline']]
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needsapproval['filename_key'] = needsapproval['filename'].apply(lambda x: x[0] if isinstance(x, list) and x else '')
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needsapproval = needsapproval.sort_values(by='filename_key').drop(columns=['filename_key'])
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needsapproval.to_parquet(f"parquet\\hashes_rep_unknown_{first_policy}_{second_policy}.parquet",index=False)
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del needsapproval
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del condensed_combo
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gc.collect()
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if not os.path.exists(f"needs_approved\\hashes_rep_good_{first_policy}_{second_policy}.csv"):
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condensed_combo = pd.read_parquet(f"parquet\\condensed_executions_{first_policy}_{second_policy}.parquet")
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needsapproval= pd.read_parquet(f"parquet\\hashes_rep_good_{first_policy}_{second_policy}.parquet")
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#Pull hash info for the entries in the needs approval table
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needsapproval = pd.merge(condensed_combo, needsapproval, on='sha256', how='inner')
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#Deduplicate lists in the columns
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for col in needsapproval.columns:
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if needsapproval[col].apply(lambda x: isinstance(x, list)).all():
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needsapproval[col] = needsapproval[col].apply(deduplicate_list)
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#Rename Publisher, Keep and reorder columns we want
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needsapproval = needsapproval.rename(columns={'publisher_x': 'publisher'})
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needsapproval = needsapproval[['sha256', 'publisher', 'description', 'filename', 'hostname', 'username', 'productname', 'productversion','reputation_lastseen', 'reputation_scannermatch', 'reputation_scannercount','reputation_status', 'reputation_threatlevel', 'reputation_threatname','reputation_timestamp', 'pprocess', 'gprocess', 'commandline']]
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needsapproval['filename_key'] = needsapproval['filename'].apply(lambda x: x[0] if isinstance(x, list) and x else '')
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needsapproval = needsapproval.sort_values(by='filename_key').drop(columns=['filename_key'])
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needsapproval.to_parquet(f"parquet\\hashes_rep_good_{first_policy}_{second_policy}.parquet", index=False)
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del needsapproval
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del condensed_combo
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gc.collect()
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if not os.path.exists(f"needs_approved\\hashes_rep_bad_{first_policy}_{second_policy}.html"):
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condensed_combo = pd.read_parquet(f"parquet\\condensed_executions_{first_policy}_{second_policy}.parquet")
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needsapproval= pd.read_parquet(f"parquet\\hashes_rep_bad_{first_policy}_{second_policy}.parquet")
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#Pull hash info for the entries in the needs approval table
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needsapproval = pd.merge(condensed_combo, needsapproval, on='sha256', how='inner')
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#Deduplicate lists in the columns
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for col in needsapproval.columns:
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if needsapproval[col].apply(lambda x: isinstance(x, list)).all():
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needsapproval[col] = needsapproval[col].apply(deduplicate_list)
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#Rename Publisher, Keep and reorder columns we want
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needsapproval = needsapproval.rename(columns={'publisher_x': 'publisher'})
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needsapproval = needsapproval[['sha256', 'publisher', 'description', 'filename', 'hostname', 'username', 'productname', 'productversion','reputation_lastseen', 'reputation_scannermatch', 'reputation_scannercount','reputation_status', 'reputation_threatlevel', 'reputation_threatname','reputation_timestamp', 'pprocess', 'gprocess', 'commandline']]
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needsapproval['filename_key'] = needsapproval['filename'].apply(lambda x: x[0] if isinstance(x, list) and x else '')
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needsapproval = needsapproval.sort_values(by='filename_key').drop(columns=['filename_key'])
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needsapproval.to_parquet(f"parquet\\hashes_rep_bad_{first_policy}_{second_policy}.parquet", index=False)
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del needsapproval
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del condensed_combo
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gc.collect()
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if os.path.exists(f"parquet\\hashes_rep_unknown_{first_policy}_{second_policy}.parquet") & os.path.exists(f"parquet\\hashes_rep_good_{first_policy}_{second_policy}.parquet") & os.path.exists(f"parquet\\hashes_rep_bad_{first_policy}_{second_policy}.parquet"):
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utils.hashfunctions.combineHashAndHist(f"parquet\\hashes_rep_unknown_{first_policy}_{second_policy}.parquet", first_policy, second_policy)
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utils.hashfunctions.combineHashAndHist(f"parquet\\hashes_rep_good_{first_policy}_{second_policy}.parquet", first_policy, second_policy)
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utils.hashfunctions.combineHashAndHist(f"parquet\\hashes_rep_bad_{first_policy}_{second_policy}.parquet", first_policy, second_policy)
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unknown = pd.read_parquet(f"parquet\\hashes_rep_unknown_{first_policy}_{second_policy}.parquet")
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good = pd.read_parquet(f"parquet\\hashes_rep_good_{first_policy}_{second_policy}.parquet")
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bad = pd.read_parquet(f"parquet\\hashes_rep_bad_{first_policy}_{second_policy}.parquet")
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@@ -423,15 +354,14 @@ def menu_prepare_to_enforce():
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unknown = unknown[~unknown["filename"].str.contains(pattern, na=False)]
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good = good[~good["filename"].str.contains(pattern, na=False)]
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unknown.to_csv(f"needs_approved\\hashes_rep_unknown_{first_policy}_{second_policy}.csv",index=False)
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good.to_csv(f"needs_approved\\hashes_rep_good_{first_policy}_{second_policy}.csv",index=False)
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bad.to_csv(f"needs_approved\\hashes_rep_bad_{first_policy}_{second_policy}.csv",index=False)
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ct.style_dataframe_dark(unknown, f"needs_approved\\hashes_rep_unknown_{first_policy}_{second_policy}.html")
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ct.style_dataframe_dark(good, f"needs_approved\\hashes_rep_good_{first_policy}_{second_policy}.html")
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ct.style_dataframe_dark(bad, f"needs_approved\\hashes_rep_bad_{first_policy}_{second_policy}.html")
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elif choice == "3":
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if os.path.exists(f"approved\\hashes_rep_unknown_{first_policy}_{second_policy}.csv") and os.path.exists(f"approved\\hashes_rep_good_{first_policy}_{second_policy}.csv"):
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@@ -454,54 +384,67 @@ def menu_prepare_to_enforce():
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if not os.path.exists(f"parquet\\path_needs_approved_{first_policy}_{second_policy}.parquet"):
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all_approved_hashes = pd.read_parquet(f"parquet\\all_approved_hashes_{first_policy}_{second_policy}.parquet")
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print(ct.colorText(f"Beginning calculating longest common filepaths for path exceptions","green"))
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grouped_df_view, df_with_groups_appended = utils.pathfunctions.export_groups_for_review(all_approved_hashes,"filename","longestcfp",min_files_for_path,path_exclusion_constant)
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df_with_groups_appended.to_parquet(f"parquet\\approved_hashes_with_paths_{first_policy}_{second_policy}.parquet", index=False)
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haslcp = utils.pathfunctions.split_filepaths_grouped(all_approved_hashes)
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haslcp.drop_duplicates()
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forbidden = utils.pathfunctions.regulator(badpathparts, True)
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forbidden_lcfp = grouped_df_view["longestcfp"].str.contains(forbidden, na=False)
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# Make a real DataFrame copy before modifying
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grouped_df_view = grouped_df_view[~forbidden_lcfp].copy()
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forbidden_lcfp = haslcp["longestcfp"].str.contains(forbidden, na=False)
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print(ct.colorText("Removing forbidden filepaths for path exceptions", "green"))
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for col in grouped_df_view.columns:
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if grouped_df_view[col].apply(lambda x: isinstance(x, list)).all():
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grouped_df_view[col] = grouped_df_view[col].apply(deduplicate_list)
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# Make a real DataFrame copy before modifying
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lcp_not_forbidden = haslcp[~forbidden_lcfp].copy()
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grouped_df_view.to_parquet(f"parquet\\path_needs_approved_{first_policy}_{second_policy}.parquet", index=False)
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grouped_df_view.to_csv(f"needs_approved\\path_needs_approved_{first_policy}_{second_policy}.csv", index=False)
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ct.style_dataframe_dark(grouped_df_view, f"needs_approved\\path_needs_approved_{first_policy}_{second_policy}.html")
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#For the review, drop down to only the columns we care, and then group by the commmon file path, consolidating and dropping dupes
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lcp_not_forbidden_review = lcp_not_forbidden[['longestcfp', 'middle', 'filename_only', 'sha256']]
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# Count unique sha256 per longestcfp
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unique_sha_counts = lcp_not_forbidden_review.groupby('longestcfp')['sha256'].nunique().reset_index()
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unique_sha_counts.columns = ['longestcfp', 'unique_sha256_count']
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# Merge the count back into the original DataFrame
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lcp_not_forbidden_review = lcp_not_forbidden_review.merge(unique_sha_counts, on='longestcfp', how='left')
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lcp_not_forbidden_review = lcp_not_forbidden_review[lcp_not_forbidden_review['unique_sha256_count'] >= min_files_for_path]
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del grouped_df_view
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del df_with_groups_appended
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gc.collect()
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lcp_not_forbidden_review.to_parquet(f"parquet\\path_needs_approved_{first_policy}_{second_policy}.parquet",index=False)
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lcp_not_forbidden_review.to_csv(f"needs_approved\\path_needs_approved_{first_policy}_{second_policy}.csv",index=False)
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else:
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print(ct.colorText(f"Please manually approve hashes prior to this step","red"))
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elif choice == "4":
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if os.path.exists(f"parquet\\approved_hashes_with_paths_{first_policy}_{second_policy}.parquet") and os.path.exists(f"approved\\path_needs_approved_{first_policy}_{second_policy}.csv"):
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if os.path.exists(f"approved\\path_needs_approved_{first_policy}_{second_policy}.csv"):
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if not os.path.exists(f"parquet\\final_hash_approvals_{first_policy}_{second_policy}.parquet") and not os.path.exists(f"parquet\\final_path_exclusions_{first_policy}_{second_policy}.parquet"):
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df1 = pd.read_parquet(f"parquet\\approved_hashes_with_paths_{first_policy}_{second_policy}.parquet")
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allowbyhash = utils.pathfunctions.mask_from_csv(df1, f"approved\\path_needs_approved_{first_policy}_{second_policy}.csv","longestcfp")
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allhashes = pd.read_parquet(f"parquet\\all_approved_hashes_{first_policy}_{second_policy}.parquet")
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pathexclusions = tryToReadCSV(f"approved\\path_needs_approved_{first_policy}_{second_policy}.csv")
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pathexclusions.to_parquet(f"parquet\\final_path_exclusions_{first_policy}_{second_policy}.parquet", index=False)
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allowbyhash = allhashes[~allhashes['sha256'].isin(pathexclusions['sha256'])]
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allowbyhash.to_parquet(f"parquet\\final_hash_approvals_{first_policy}_{second_policy}.parquet", index=False)
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easyview = allowbyhash.groupby('sha256').agg(list).reset_index()
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# Deduplicate all list columns in easyview
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for col in easyview.columns:
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if col != 'sha256': # Skip the grouping column
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easyview[col] = easyview[col].apply(lambda x: list(set(x)))
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easyview = easyview.sort_values(by=["reputation_status", "filename"])
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ct.style_dataframe_dark(easyview, f"preflight\\final_hash_approvals_{first_policy}_{second_policy}.html")
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ct.style_dataframe_dark(pathexclusions, f"preflight\\final_path_exclusions_{first_policy}_{second_policy}.html")
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del df1
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del allowbyhash
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del pathexclusions
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del easyview
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@@ -542,7 +485,19 @@ def menu_prepare_to_enforce():
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print(ct.colorText("Proceeding with the code...", "yellow"))
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print(ct.colorText(f"Adding path exclusions to {destination_name}", "yellow"))
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pathexcludelist = pathexclusions['longestcfp'].unique().tolist()
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utils.policyfunctions.addPath(destination_id,pathexcludelist)
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# Regex to match a Windows drive letter at the start (e.g., C:\)
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drive_letter_pattern = re.compile(r'^[a-zA-Z]:\\')
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# Processed list
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processed_paths = [
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(path if drive_letter_pattern.match(path) else f"\\\\{path}") + "**"
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for path in pathexcludelist
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]
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utils.policyfunctions.addPath(destination_id,processed_paths)
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print(ct.colorText(f"Adding hashes to {allowlist_parent_name}", "yellow"))
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@@ -554,6 +509,8 @@ def menu_prepare_to_enforce():
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utils.policyfunctions.addHash(allowlist_child_id, allowlist_childhashlist)
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ct.locked()
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print(repr(processed_paths))
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print(processed_paths)
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exit()
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else:
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+22
-3
@@ -19,6 +19,7 @@ import os
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import json
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import utils.pathfunctions as pathf
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import utils.pretty as ct
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import gc
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def aggregateHashes(executions_json) -> pd.DataFrame:
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@@ -103,7 +104,7 @@ def augmentAggregatedHashes(url, agg_df: pd.DataFrame) -> pd.DataFrame:
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return aug_df
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def categorizeHashes(df: pd.DataFrame, threat_tolerance: int, untrusted_publishers: list, pups: list):
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def categorizeHashes(df: pd.DataFrame, threat_tolerance: int, untrusted_publishers, pups: list):
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if untrusted_publishers is None: untrusted_publishers = []
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if pups is None: pups = []
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@@ -126,14 +127,14 @@ def categorizeHashes(df: pd.DataFrame, threat_tolerance: int, untrusted_publishe
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mask_approved = (
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(
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(df["publisher"] != "Not Signed") &
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~df["publisher"].isin(untrusted_publishers) &
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~df["publisher"].str.contains(pathf.regulator(untrusted_publishers), case=False, na=False) &
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~df["reputation_status"].isna() &
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~df["description"].str.contains(pathf.regulator(pups), case=False, na=False)
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) |
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(
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(df["publisher"] == "Not Signed") &
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~df["reputation_flag"] &
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~df["publisher"].isin(untrusted_publishers) &
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~df["publisher"].str.contains(pathf.regulator(untrusted_publishers), case=False, na=False) &
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~df["reputation_status"].isna() &
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~df["description"].str.contains(pathf.regulator(pups), case=False, na=False)
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)
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@@ -203,3 +204,21 @@ def destinationHashes(
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# Concatenate results
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df_hashdestination = pd.concat([df_paths, df_hashes], ignore_index=True)
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return df_hashdestination
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def combineHashAndHist(path, first_policy, second_policy):
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condensed_combo = pd.read_parquet(f"parquet\\condensed_executions_{first_policy}_{second_policy}.parquet")
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df = pd.read_parquet(path)
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#Pull hash info for the entries in the needs approval table
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df = pd.merge(condensed_combo, df, on='sha256', how='inner')
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#Rename Publisher, Keep and reorder columns we want
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df = df.rename(columns={'publisher_x': 'publisher'})
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df = df[['sha256', 'publisher', 'description', 'filename', 'hostname', 'username', 'productname', 'productversion','reputation_lastseen', 'reputation_scannermatch', 'reputation_scannercount','reputation_status', 'reputation_threatlevel', 'reputation_threatname','reputation_timestamp', 'pprocess', 'gprocess', 'commandline']]
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df = df.sort_values(by='filename')
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df.to_parquet(path, index=False)
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del df
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del condensed_combo
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gc.collect()
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+45
-58
@@ -15,73 +15,60 @@
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import pandas as pd
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import os
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from itertools import chain
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import ast
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import re
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def split_path(path):
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parts = []
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while True:
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head, tail = os.path.split(path)
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if tail:
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parts.insert(0, tail)
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path = head
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else:
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if head:
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parts.insert(0, head)
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break
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return parts
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import os
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import pandas as pd
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def local_common_pass(paths, min_parts=3):
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results = {}
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paths_sorted = sorted(paths)
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for i, path in enumerate(paths_sorted):
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candidates = []
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import os
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import pandas as pd
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if i > 0:
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try:
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candidates.append(os.path.commonpath([path, paths_sorted[i-1]]))
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except ValueError:
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# different drives, skip
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pass
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if i < len(paths_sorted) - 1:
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try:
|
||||
candidates.append(os.path.commonpath([path, paths_sorted[i+1]]))
|
||||
except ValueError:
|
||||
# different drives, skip
|
||||
pass
|
||||
def split_filepaths_grouped(df, col="filename", group_parts=3, min_parts=3):
|
||||
def clean_split(path):
|
||||
parts = os.path.normpath(path).split(os.sep)
|
||||
# Remove leading empty strings caused by UNC paths
|
||||
parts = [p for p in parts if p]
|
||||
return parts
|
||||
|
||||
best = path
|
||||
best_len = 0
|
||||
for c in candidates:
|
||||
parts = split_path(c)
|
||||
if len(parts) >= min_parts and len(parts) > best_len:
|
||||
best = c
|
||||
best_len = len(parts)
|
||||
results[path] = best
|
||||
return results
|
||||
df = df.copy()
|
||||
split_paths = df[col].apply(clean_split)
|
||||
|
||||
def add_longest_common_two_local(df, col="filename_x", new_col="longestcfp", min_parts=3):
|
||||
dirs_series = df[col].astype(str).apply(os.path.dirname)
|
||||
first_pass = local_common_pass(dirs_series.tolist(), min_parts)
|
||||
second_pass = local_common_pass(list(first_pass.values()), min_parts)
|
||||
df[new_col] = dirs_series.map(lambda d: second_pass[first_pass[d]])
|
||||
return df
|
||||
# Filter out paths with fewer than `min_parts` components
|
||||
df = df[split_paths.apply(lambda parts: len(parts) >= min_parts)].copy()
|
||||
split_paths = split_paths[df.index] # Update split_paths to match filtered df
|
||||
|
||||
def export_groups_for_review(df, col, group_col, min_number_in_group, path_length_constant):
|
||||
"""
|
||||
Compute longest common paths, group filepaths, write CSV for review.
|
||||
"""
|
||||
df = df.drop_duplicates(subset=[col], keep='first')
|
||||
df = add_longest_common_two_local(df, col=col, new_col=group_col)
|
||||
grouped = df.groupby(group_col)[col].apply(list).reset_index()
|
||||
grouped = grouped.sort_values(by=col)
|
||||
print("Before filtering:", len(grouped))
|
||||
grouped = grouped[grouped[col].apply(lambda x: len(x) >= min_number_in_group)]
|
||||
filtered = grouped[grouped[group_col].apply(lambda x: len(os.path.normpath(x).split(os.sep)) >= path_length_constant)]
|
||||
print("After filtering:", len(grouped))
|
||||
df["group_key"] = split_paths.apply(lambda parts: os.sep.join(parts[:group_parts]))
|
||||
grouped = df.groupby("group_key")
|
||||
new_rows = []
|
||||
|
||||
return filtered, df
|
||||
for _, group_df in grouped:
|
||||
paths = group_df[col].tolist()
|
||||
split_parts = [clean_split(p) for p in paths]
|
||||
|
||||
def longest_common_prefix(paths):
|
||||
if not paths:
|
||||
return []
|
||||
prefix = paths[0]
|
||||
for path in paths[1:]:
|
||||
prefix = [a for a, b in zip(prefix, path) if a == b]
|
||||
if not prefix:
|
||||
break
|
||||
return prefix
|
||||
|
||||
common_prefix = longest_common_prefix(split_parts)
|
||||
prefix_str = os.sep.join(common_prefix)
|
||||
|
||||
for i, parts in enumerate(split_parts):
|
||||
filename = parts[-1]
|
||||
middle = os.sep.join(parts[len(common_prefix):-1]) if len(parts) > len(common_prefix) + 1 else ""
|
||||
row = group_df.iloc[i].copy()
|
||||
row["longestcfp"] = prefix_str
|
||||
row["middle"] = middle
|
||||
row["filename_only"] = filename
|
||||
new_rows.append(row)
|
||||
|
||||
return pd.DataFrame(new_rows).drop(columns=["group_key"])
|
||||
|
||||
def mask_from_csv(df, csv_path, filepath_col):
|
||||
"""
|
||||
|
||||
Reference in New Issue
Block a user