marco/ids.alfacom.it
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Update detection results to use correct key names for scores

Browse Source 
Corrects the key names used to retrieve detection results in `compare_models.py` to match the output format of the hybrid detector.

Replit-Commit-Author: Agent
Replit-Commit-Session-Id: 7a657272-55ba-4a79-9a2e-f1ed9bc7a528
Replit-Commit-Checkpoint-Type: full_checkpoint
Replit-Commit-Event-Id: 600ade79-ad9b-4993-b968-e6466b703598
Replit-Commit-Screenshot-Url: https://storage.googleapis.com/screenshot-production-us-central1/449cf7c4-c97a-45ae-8234-e5c5b8d6a84f/7a657272-55ba-4a79-9a2e-f1ed9bc7a528/RJGlbTt
This commit is contained in:
marco370 2025-11-25 08:51:27 +00:00
parent 49eb9a9f91
commit 837f7d4c08
2 changed files with 13 additions and 10 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
.replit
View File

@ -18,10 +18,6 @@ externalPort = 80
localPort = 41303
externalPort = 3002
[[ports]]
localPort = 41797
externalPort = 3001
[[ports]]
localPort = 43471
externalPort = 3003

19
python_ml/compare_models.py
View File

@ -116,6 +116,13 @@ def reanalyze_with_hybrid(detector, ip_address, old_detection):
new_detection = result[0]
# Confronto
new_score = new_detection.get('risk_score', 0)
new_type = new_detection.get('anomaly_type', 'unknown')
new_confidence = new_detection.get('confidence_level', 'unknown')
# Determina se è anomalia (score >= 80 = critical threshold)
new_is_anomaly = new_score >= 80
comparison = {
'ip_address': ip_address,
'logs_count': len(logs),
@ -126,14 +133,14 @@ def reanalyze_with_hybrid(detector, ip_address, old_detection):
'old_blocked': old_detection['blocked'],
# Nuovo modello Hybrid (rianalisi)
'new_score': new_detection.get('anomaly_score', 0),
'new_anomaly_type': new_detection.get('anomaly_type', 'unknown'),
'new_confidence': new_detection.get('confidence', 'unknown'),
'new_is_anomaly': new_detection.get('is_anomaly', False),
'new_score': new_score,
'new_anomaly_type': new_type,
'new_confidence': new_confidence,
'new_is_anomaly': new_is_anomaly,
# Delta
'score_delta': new_detection.get('anomaly_score', 0) - float(old_detection['risk_score']),
'type_changed': old_detection['anomaly_type'] != new_detection.get('anomaly_type', 'unknown'),
'score_delta': new_score - float(old_detection['risk_score']),
'type_changed': old_detection['anomaly_type'] != new_type,
}
return comparison