mysticmomba/AirlockTools
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Reverting Back to Working Branch

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brotoskyj
2025-08-25 12:14:58 -04:00
parent 05ca60f1b6 b187d8cd79
commit 3c265c5817
4 changed files with 65 additions and 36 deletions
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utils/hashfunctions.py
+22 -12
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@@ -90,6 +90,7 @@ def categorizeHashes(aug_df: pd.DataFrame, threat_tolerance: int, untrusted_publ
df = aug_df.copy()
<<<<<<< HEAD
<<<<<<< HEAD
def reputationtool(row):
val = row["reputation_scannermatch"]
@@ -100,11 +101,17 @@ def categorizeHashes(aug_df: pd.DataFrame, threat_tolerance: int, untrusted_publ
if row["reputation_scannermatch"] == "N/A":
return True
>>>>>>> ccbf716 (Zar-Branch (#6))
=======
def reputationtool(row, threat_tolerance):
if row["reputation_scannermatch"] == "N/A":
return True
>>>>>>> b187d8cd79c04d364185a4e366d0432841a3372a
try:
return int(val) > threat_tolerance
except (ValueError, TypeError):
return row["publisher_y"] == "Not Signed"
<<<<<<< HEAD
<<<<<<< HEAD
df["reputation_flag"] = df.apply(reputationtool, axis=1)
@@ -112,25 +119,25 @@ def categorizeHashes(aug_df: pd.DataFrame, threat_tolerance: int, untrusted_publ
((df["publisher_y"] == "Not Signed") & df["reputation_flag"]) |
(df["reputation_status"] == "UNKNOWN")
)
=======
mask_needsreview = (df["publisher_y"] == "Not Signed") & df.apply(lambda row: reputationtool(row, threat_tolerance), axis=1)
>>>>>>> b187d8cd79c04d364185a4e366d0432841a3372a
mask_approved = (
(
(df["publisher_y"] != "Not Signed") &
~df["publisher_y"].isin(untrusted_publishers) &
~df["reputation_status"].isna()
) |
(
(df["publisher_y"] == "Not Signed") &
~df["reputation_flag"] &
~df["publisher_y"].isin(untrusted_publishers) &
~df["reputation_status"].isna()
)
((df["publisher_y"] != "Not Signed") & (~df["publisher_y"].isin(untrusted_publishers))) &
(df["publisher_y"] != "Not Signed") # explicitly signed
) | (
(df["publisher_y"] == "Not Signed") &
(~df.apply(lambda row: reputationtool(row, threat_tolerance), axis=1)) &
(~df["publisher_y"].isin(untrusted_publishers)) # exclude untrusted even if unsigned
)
needsreview_df = df[mask_needsreview]
approved_df = df[mask_approved]
unapproved_df = df[~(mask_needsreview | mask_approved)]
remaining_df = df[~(mask_needsreview | mask_approved)]
<<<<<<< HEAD
return needsreview_df, approved_df, unapproved_df
=======
mask_needsreview = (df["publisher_y"] == "Not Signed") & df.apply(lambda row: reputationtool(row, threat_tolerance), axis=1)
@@ -151,3 +158,6 @@ def categorizeHashes(aug_df: pd.DataFrame, threat_tolerance: int, untrusted_publ
return needsreview_df, approved_df, remaining_df
>>>>>>> ccbf716 (Zar-Branch (#6))
=======
return needsreview_df, approved_df, remaining_df
>>>>>>> b187d8cd79c04d364185a4e366d0432841a3372a