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feat(scoring): Implement dedicated scoring logic for URL analysis #2628

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feat(scoring): Implement dedicated scoring logic for URL analysis #2628

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149 changes: 61 additions & 88 deletions lib/cuckoo/common/scoring.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,30 @@
# =============================================================================
# Helper function to prevent code duplication for generic scoring.
# =============================================================================
def _calculate_generic_score(matched: list) -> float:
"""Calculates a generic score based on a list of matched signatures."""
score = 0.0
for match in matched:
# We apply the 'maximum' attribute if present in the signature.
# Check for key existence and that the value is not None to handle the case where maximum could be 0.
if "maximum" in match and match["maximum"] is not None:
score = max(score, match["maximum"])
continue # Skip to next signature

if match["severity"] == 1:
score += match["weight"] * 0.5 * (match["confidence"] / 100.0)
else:
score += match["weight"] * (match["severity"] - 1) * (match["confidence"] / 100.0)

# Clamp the score between 0.0 and 10.0 using a common Python idiom.
score = max(0.0, min(score, 10.0))

return score


# =============================================================================
# Main scoring function.
# =============================================================================
def calc_scoring(results: dict, matched: list):
"""
Calculate the final malware score and status based on the analysis results and matched signatures.
Expand Down Expand Up @@ -25,35 +52,34 @@ def calc_scoring(results: dict, matched: list):
"""
finalMalscore = 0.0
status = None
# Identify the analysis category (file or url).
category = results.get("target", {}).get("category")
fileType = results.get("target", {}).get("file", {}).get("type")

# IF THE ANALYSIS IS OF URL TYPE, we use the generic scoring logic
if category == "url":
# Calculate score using the helper function
finalMalscore = _calculate_generic_score(matched)

# We assign a status based on the score
if finalMalscore >= 7.0:
status = "Malicious"
elif finalMalscore >= 4.0:
status = "Suspicious"
elif finalMalscore > 0.0:
status = "Clean"
else:
status = "Undetected"

return finalMalscore, status

if not fileType:
return finalMalscore, status

if "executable" in fileType:
# We have 5 methodologies
# 1. The file is Malicious-Known (The sample is detected by YARA)
## score 10/10 (Malicious)
# =======================================================================================================#
# 2. If the file is Malicious-Unknown
## triggered some signatures that has specific malicious categories such as:
## ["malware", "ransomware", "infostealer", "rat", "trojan", "rootkit", "bootkit", "wiper", "banker",
## "bypass", "anti-sandbox", "keylogger"]
## score [7-9]/10 (Malicious)
# =======================================================================================================#
# 3. If the file is Suspicious-Unknown
## triggered some signatures that has specific suspicious categories such as:
## ["network", "encryption", "anti-vm", "anti-analysis", "anti-av", "anti-debug", "anti-emulation",
## "persistence", "stealth", "discovery", "injection", "generic", "account", "bot", "browser",
# "allocation", "command"]
## score[4-6]/10 (Suspicious)
# =======================================================================================================#
# 4. If the file is benign
## Likely all trusted files are digitally signed.
## score [0-3]/10 (benign)
# =======================================================================================================#
# 5. If the file doesn't trigger any signatures
## The file is undetected/failed
# ... (and so on, this logic is specific to executables)
tempScore1 = 0.0
tempScore2 = 0.0
is_maliciousCategoryHit = False
Expand All @@ -66,39 +92,14 @@ def calc_scoring(results: dict, matched: list):
)

maliciousCategories = [
"malware",
"ransomware",
"infostealer",
"rat",
"trojan",
"rootkit",
"bootkit",
"wiper",
"banker",
"bypass",
"anti-sandbox",
"keylogger",
"malware", "ransomware", "infostealer", "rat", "trojan", "rootkit", "bootkit", "wiper", "banker",
"bypass", "anti-sandbox", "keylogger",
]

suspiciousCategories = [
"network",
"encryption",
"anti-vm",
"anti-analysis",
"anti-av",
"anti-debug",
"anti-emulation",
"persistence",
"stealth",
"discovery",
"injection",
"generic",
"account",
"bot",
"browser",
"allocation",
"command",
"execution",
"network", "encryption", "anti-vm", "anti-analysis", "anti-av", "anti-debug", "anti-emulation",
"persistence", "stealth", "discovery", "injection", "generic", "account", "bot", "browser",
"allocation", "command", "execution",
]

for detection in results.get("detections", []):
Expand All @@ -122,74 +123,46 @@ def calc_scoring(results: dict, matched: list):
else:
tempScore2 += matchedSig["weight"] * (matchedSig["severity"] - 1) * (matchedSig["confidence"] / 100.0)

# 1. The file is Malicious-Known (The sample is detected by YARA)
## score 10/10 (Malicious)
# 1. Malicious-Known
if is_detected:
status = "Malicious"
finalMalscore = 10.0

# 2. If the file is Malicious-Unknown
## triggered some signatures that has specific malicious categories such as:
## ["malware", "ransomware", "infostealer", "rat", "trojan", "rootkit", "bootkit", "wiper", "banker",
## "bypass", "anti-sandbox", "keylogger"]
## score [7-9]/10 (Malicious)
# 2. Malicious-Unknown
elif is_maliciousCategoryHit:
finalMalscore = tempScore1
status = "Malicious"
## Include numbers between that range
if 7.0 < finalMalscore < 9.0:
pass
elif finalMalscore >= 9.0:
finalMalscore = 9.0
elif finalMalscore < 7.0:
finalMalscore = 7.0

# 3. If the file is Suspicious-Unknown
## triggered some signatures that has specific suspicious categories such as:
## ["network", "encryption", "anti-vm", "anti-analysis", "anti-av", "anti-debug", "anti-emulation",
## "persistence", "stealth", "discovery", "injection", "generic", "account", "bot", "browser",
# "allocation", "command"]
## score[4-6]/10 (Suspicious)
# 3. Suspicious-Unknown
elif is_suspiciousCategoryHit:
finalMalscore = tempScore2

# 4. If the file is benign
## Likely all trusted files are digitally signed.
## score [0-3]/10 (benign)
if is_digital_signauture_verified:
finalMalscore = 0.0
status = "Clean"

elif finalMalscore < 4.0:
status = "Clean"

## Include numbers between that range
elif 4.0 < finalMalscore < 6.0:
status = "Suspicious"
elif finalMalscore == 4:
finalMalscore = 4
status = "Suspicious"
elif finalMalscore >= 6.0:
finalMalscore = 6.0
status = "Suspicious"
elif 4.0 <= finalMalscore < 6.0:
status = "Suspicious"

# 5. If the file doesn't trigger any signatures
## The file is undetected/failed
# 5. Undetected/Failed
else:
finalMalscore = 0
if results.get("behavior", {}).get("processtree", []):
status = "Undetected"
else:
status = "Failed"
else:
for match in matched:
if match["severity"] == 1:
finalMalscore += match["weight"] * 0.5 * (match["confidence"] / 100.0)
else:
finalMalscore += match["weight"] * (match["severity"] - 1) * (match["confidence"] / 100.0)
if finalMalscore > 10.0:
finalMalscore = 10.0
if finalMalscore < 0.0:
finalMalscore = 0.0
# For all other non-executable file types, use the generic scoring logic
finalMalscore = _calculate_generic_score(matched)
# Note: The original logic did not assign a status here, so we keep that behavior.

return finalMalscore, status