213 lines
8.6 KiB
Python
213 lines
8.6 KiB
Python
# Copyright (C) 2025 James Brotosky, Brandon Wickline
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#
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero General Public License as published
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# by the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU Affero General Public License for more details.
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#
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# You should have received a copy of the GNU Affero General Public License
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# along with this program. If not, see <https://www.gnu.org/licenses/>.
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#Local Imports
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import utils.hashfunctions as hashf
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import utils.pathfunctions as pathf
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import utils.utils as ct
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#Standard Libary Imports:
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import os
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import re
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#3rd Party Imports:
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import pandas as pd
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def split_filepaths_grouped(df, col="filename", group_parts=4, min_parts=4):
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def clean_split(path):
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parts = os.path.normpath(path).split(os.sep)
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# Remove leading empty strings caused by UNC paths
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parts = [p for p in parts if p]
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return parts
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df = df.copy()
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split_paths = df[col].apply(clean_split)
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# Filter out paths with fewer than `min_parts` components
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df = df[split_paths.apply(lambda parts: len(parts) >= min_parts)].copy()
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split_paths = split_paths[df.index] # Update split_paths to match filtered df
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df["group_key"] = split_paths.apply(lambda parts: os.sep.join(parts[:group_parts]))
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grouped = df.groupby("group_key")
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new_rows = []
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for _, group_df in grouped:
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paths = group_df[col].tolist()
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split_parts = [clean_split(p) for p in paths]
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def longest_common_prefix(paths):
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if not paths:
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return []
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prefix = paths[0]
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for path in paths[1:]:
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prefix = [a for a, b in zip(prefix, path) if a == b]
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if not prefix:
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break
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return prefix
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common_prefix = longest_common_prefix(split_parts)
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prefix_str = os.sep.join(common_prefix)
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for i, parts in enumerate(split_parts):
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filename = parts[-1]
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middle = os.sep.join(parts[len(common_prefix):-1]) if len(parts) > len(common_prefix) + 1 else ""
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row = group_df.iloc[i].copy()
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row["longestcfp"] = prefix_str
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row["middle"] = middle
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row["filename_only"] = filename
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row["file_extension"] = os.path.splitext(filename)[1].lower()
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new_rows.append(row)
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return pd.DataFrame(new_rows).drop(columns=["group_key"])
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def inspect_parquet(path):
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try:
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df = pd.read_parquet(path)
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print(f"✅ Successfully read: {path}")
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print(f"📄 Columns: {df.columns.tolist()}")
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print(f"🔢 Rows: {len(df)}")
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return df
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except Exception as e:
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print(f"❌ Error reading {path}: {e}")
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return pd.DataFrame()
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def regulator(paths, case_insensitive=True):
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"""
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Build a regex pattern that matches any of the given Windows path fragments.
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"""
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escaped = [re.escape(p) for p in paths]
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pattern = "(?:" + "|".join(escaped) + ")"
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if case_insensitive:
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pattern = "(?i)" + pattern # Add inline case-insensitive flag
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print(f"Regulator is providing: {pattern}")
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return pattern
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def calculatePath(approved_hashes, badpathparts, path_exclusion_constant, min_files_for_path, split):
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if split : dfs_by_policy = [group for _, group in approved_hashes.groupby('policy')]
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else : dfs_by_policy = [approved_hashes]
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processed_dfs = []
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for df in dfs_by_policy:
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haslcp = pathf.split_filepaths_grouped(df, "filename", path_exclusion_constant, min_files_for_path)
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haslcp = haslcp.drop_duplicates()
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forbidden = pathf.regulator(badpathparts, True)
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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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lcp_not_forbidden = haslcp[~forbidden_lcfp].copy()
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lcp_not_forbidden_review = lcp_not_forbidden[['policy', 'longestcfp', 'middle', 'filename_only', 'file_extension', 'sha256']]
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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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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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processed_dfs.append(lcp_not_forbidden_review)
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pathExclusions = pd.concat(processed_dfs, ignore_index=True)
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return pathExclusions
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def generatePathReview(unknown, good, badpathparts, path_exclusion_constant, min_files_for_path, split = False):
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df1 = ct.tryToReadCSV(unknown)
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df2 = ct.tryToReadCSV(good)
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all_approved_hashes = pd.concat([df1 , df2], ignore_index=True).sort_values(by=['filename'])
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primary_path_exclusions = calculatePath(all_approved_hashes, badpathparts, path_exclusion_constant, min_files_for_path, split)
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remaining_hashes = all_approved_hashes[~all_approved_hashes['sha256'].isin(primary_path_exclusions['sha256'])]
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secondary_path_exclusions = calculatePath(remaining_hashes, badpathparts, 3, min_files_for_path, split)
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remaining_hashes = remaining_hashes[~remaining_hashes['sha256'].isin(secondary_path_exclusions['sha256'])]
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return all_approved_hashes, primary_path_exclusions, secondary_path_exclusions, remaining_hashes
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def clean_folders(parq_base_dir, appr_base_dir, needappr_base_dir, pflight_base_dir):
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"""
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Prompts user to choose whether to delete all .parquet files or preserve execution_history ones.
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Then deletes .csv, .html, and .parquet files accordingly from specified folders.
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"""
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# Prompt user
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user_input = input("Do you want to delete *all* .parquet files including execution_history ones? (yes/y or no/n): ").strip().lower()
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delete_execution_hist = user_input in ["yes", "y"]
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folders = [parq_base_dir, appr_base_dir, needappr_base_dir, pflight_base_dir]
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for folder in folders:
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folder_path = os.path.abspath(folder)
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if not os.path.isdir(folder_path):
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print(f"Folder not found: {folder_path}")
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continue
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for filename in os.listdir(folder_path):
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file_path = os.path.join(folder_path, filename)
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if not os.path.isfile(file_path):
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continue
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_, ext = os.path.splitext(filename)
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# Delete .csv and .html files
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if ext in [".csv", ".html"]:
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os.remove(file_path)
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print(f"Deleted: {file_path}")
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# Delete .parquet files based on user choice
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elif ext == ".parquet":
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if delete_execution_hist or not filename.startswith("execution_history"):
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os.remove(file_path)
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def generateApprovables(needappr_base_dir, appr_base_dir, parq_base_dir, badpathparts, bad_publisher_list, path_exclusion_constant, min_files_for_path, split= False ):
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if os.path.exists(f"{needappr_base_dir}unknown_hashes.csv") and os.path.exists(f"{needappr_base_dir}good_hashes.csv"):
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all_hashes, primary_paths, secondary_paths, remaining = pathf.generatePathReview(f"{appr_base_dir}unknown_hashes.csv", f"{appr_base_dir}good_hashes.csv", badpathparts,path_exclusion_constant, min_files_for_path, split)
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all_hashes.to_parquet(f"{parq_base_dir}all_hashes.parquet", index=False)
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dataframes = {
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"all_hashes" : all_hashes,
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"primary_Paths": primary_paths,
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"secondary_Paths": secondary_paths,
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"remaining": remaining
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}
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for name, df in dataframes.items():
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df.to_csv(f"{needappr_base_dir}{name}.csv", index=False)
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df.to_parquet(f"{parq_base_dir}{name}.parquet", index=False)
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ct.style_dataframe_dark(df, f"{needappr_base_dir}{name}.html")
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publishers = hashf.generatePublist(f"{parq_base_dir}all_hashes.parquet",bad_publisher_list)
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publishers.to_csv(f"{needappr_base_dir}publishers.csv", index=False)
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publishers.to_parquet(f"{parq_base_dir}publishers.parquet", index=False)
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ct.style_dataframe_dark(publishers, f"{needappr_base_dir}publishers.html")
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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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