Executive Summary
This report covers threat intelligence collected by HookProbe's edge IDS deployment during May 2026. All data comes from a production Raspberry Pi 5 running the NAPSE AI-native intrusion detection engine, HYDRA threat intelligence pipeline, and AEGIS autonomous defense system.
HookProbe processed 0 security events this month, classified 0 ML verdicts, tracked 0 unique IP addresses, and analyzed 0 network flows totaling 0 GB of traffic.
Threat Event Breakdown
The HYDRA threat intelligence pipeline processed 0 events across three defense layers:
| Defense Layer | Events | Unique IPs | % of Total |
|---|---|---|---|
| Rate Limiting (DDoS/Brute-Force) | 0 | 0 | 0% |
| Blocklist Enforcement | 0 | 0 | 0% |
| ML Score Threshold | 0 | 0 | 0% |
ML Classification Results
The SENTINEL ML ensemble classified 0 IP behaviors this month:
- Benign: 0 (0%) — normal traffic, no action taken
- Suspicious: 0 (0%) — elevated monitoring, behavioral tracking
- Malicious: 0 (0%) — escalated to cognitive throttling or blocking
IP Risk Distribution
HYDRA profiled 0 unique IP addresses with composite risk scores:
- Critical (0.8+): 0 IPs (0%)
- High (0.5-0.8): 0 IPs (0%)
- Medium (0.2-0.5): 0 IPs (0%)
- Low (<0.2): 0 IPs (0%)
Indicators of Compromise
0 new IoCs were discovered this month (0 active total). All indicators are IP-based, sourced from behavioral analysis by the SENTINEL ML pipeline and correlated with Spamhaus DROP and FireHOL blocklists.
Attack Pattern Intelligence
The SENTINEL pattern mining engine discovered 0 attack patterns and identified 0 coordinated campaigns. The predictive engine generated 0 proactive alerts for preemptive defense.
Network Flow Analysis
NAPSE processed 0 network flows totaling 0 GB of inspected traffic. Autonomous blocking issued 0 throttle/block actions against 0 unique IPs.
Security Posture
The QSecBit security score averaged 0/100 throughout May 2026, maintaining GREEN (Protected) status. The score remained stable, indicating consistent defense posture without degradation events.
Key Takeaways
- Rate limiting remains the primary defense mechanism, handling 0% of all security events from just 0 aggressive source IPs
- The ML pipeline correctly identified 0% of traffic as benign — low false positive rate
- 0 critical-risk IPs were identified and tracked — representing active threat actors
- All detection and response ran autonomously on a Raspberry Pi 5 with zero manual intervention
About This Report
This threat intelligence is generated from a production HookProbe deployment running on a Raspberry Pi 5 (8GB RAM). The system uses NAPSE (AI-native IDS), HYDRA (threat intelligence pipeline), SENTINEL (ML classification), and AEGIS (autonomous defense) — all open-source under AGPL v3.0.
Data is collected, processed, and published automatically. No data is fabricated or simulated. View the source code on GitHub.