dc91096d9d
Update log processing to use the correct timestamp field and introduce cron jobs for automated model training and anomaly detection. Replit-Commit-Author: Agent Replit-Commit-Session-Id: 7a657272-55ba-4a79-9a2e-f1ed9bc7a528 Replit-Commit-Checkpoint-Type: full_checkpoint Replit-Commit-Event-Id: f0653fd5-fc94-4fcb-8d7e-2a0e90fc81bf Replit-Commit-Screenshot-Url: https://storage.googleapis.com/screenshot-production-us-central1/449cf7c4-c97a-45ae-8234-e5c5b8d6a84f/7a657272-55ba-4a79-9a2e-f1ed9bc7a528/MkBJZ0L
36 lines
949 B
Bash
36 lines
949 B
Bash
#!/bin/bash
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# =========================================================
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# CRON TRAINING - Addestramento automatico modello ML
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# =========================================================
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# Esegue training ogni 12 ore con 100K log più recenti
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# =========================================================
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# Logging
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LOG_FILE="/var/log/ids/training.log"
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mkdir -p /var/log/ids
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exec >> "$LOG_FILE" 2>&1
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echo "========================================="
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echo "🤖 [$(date)] TRAINING AUTOMATICO AVVIATO"
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echo "========================================="
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# Esegue training via API
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curl -X POST http://localhost:8000/train \
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-H "Content-Type: application/json" \
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-d '{
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"max_records": 100000,
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"hours_back": 24,
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"contamination": 0.01
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}' \
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--max-time 300
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EXIT_CODE=$?
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if [ $EXIT_CODE -eq 0 ]; then
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echo "✅ [$(date)] Training completato con successo"
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else
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echo "❌ [$(date)] Training fallito (exit code: $EXIT_CODE)"
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fi
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echo ""
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