
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| UA bot: crawler | Known bot/crawler User-Agent detected | +40 | |
| Burst: 12 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 17 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 19 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 33 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 12 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| Burst: 5 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 23 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 20 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 14 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 14 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 7 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Probe pattern 302->404 same path | Behavioral anomaly detected by automated analysis | +20 | |
| Burst: 17 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 24 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 13 req / 10s | Abnormally fast request rate — automated scanning | +35 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Address UA spoofing from 144.76.32.189: maintain blocklist of known malicious UA strings, require consistent UA across sessions, implement TLS fingerprinting.
Implement limit_req_zone in nginx. Deploy CDN with DDoS protection. Configure SYN cookies and connection tracking to throttle 144.76.32.189.
Block scanning from 144.76.32.189: rate-limit 404 responses per IP, deploy a honeypot 404 page, ensure no backup files are web-accessible.
Other blocked IPs from the same /24 subnet — indicates systematic abuse from this network range.
This IP was checked against major DNS-based blacklists used by mail servers and firewalls worldwide.
Checked: Spamhaus, SpamCop, Barracuda, SORBS, CBL, UCEProtect. Results may change over time.
144.76.32.189 has been assigned a threat score of 110/100 (Critical). With this rating, the IP falls into the critical severity bracket — among the most dangerous addresses in our monitoring database.
The following attack categories were identified:
The address 144.76.32.189 originates from Falkenstein, Germany, operating on the network of Hetzner Online GmbH. It was identified through automated analysis of incoming network traffic across monitored endpoints. The address has been active for 83 days in our monitoring system, producing 29 flagged requests at a rate of ~0.3/day. This address belongs to a datacenter or cloud hosting provider. Hosting IPs are frequently leveraged by threat actors who rent cheap VPS instances specifically for conducting attacks. The diversity of 3 separate attack methods suggests a comprehensive attack toolkit — likely an automated scanner that tests for vulnerabilities across multiple categories. Germany currently accounts for 166 blocked IPs in our database, making it a significant source of malicious traffic. With a threat score of 110/100, this IP is among the most dangerous addresses in our database. Immediate and complete blocking is strongly recommended.
This IP belongs to a hosting or data center provider. Malicious traffic from hosting infrastructure often originates from compromised VPS instances, rented servers used for scanning campaigns, or abused free-tier cloud accounts. Hosting providers typically respond to abuse reports within 24-72 hours.
TLS fingerprinting creates unique identifiers based on how clients negotiate encrypted connections. The JA3 and JA4 methods generate hashes from TLS ClientHello parameters, enabling identification of specific tools and malware regardless of IP address changes.
Correlating logs across web servers, firewalls, DNS, and authentication systems reveals attack patterns invisible in individual log sources. Modern SIEM platforms use statistical analysis to connect related events across time and systems.