
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| UA changed for same IP | Multiple User-Agents — bot rotation technique | +25 | |
| Danger strong hits: 14 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Burst: 40 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 66 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger strong hits: 57 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 1 | Medium-risk: admin panels, config files | +10 | |
| 404 ratio >= 60% | Majority of requests returned 404 — enumeration | +25 | |
| Burst: 34 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 111 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger strong hits: 102 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Burst: 49 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 163 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger strong hits: 8 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 28 | Medium-risk: admin panels, config files | +60 | |
| Burst: 19 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 28 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 45.86.200.130: 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 45.86.200.130.
Block scanning from 45.86.200.130: 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.
45.86.200.130 has been assigned a threat score of 255/100 (Critical). A score this high marks a critical threat actor. This address has demonstrated persistent, aggressive malicious behavior across multiple detection vectors.
The following attack categories were identified:
Our monitoring infrastructure has identified 45.86.200.130, geolocated to The Hague, Netherlands, operating on the network of F.N.S. HOLDINGS LIMITED, as a source of suspicious network activity. Our sensors captured 4 malicious requests from this address across a 6-day span, reflecting a sustained attack cadence of ~0.7 requests per day. The address operates as a VPN/proxy exit node. Attackers route traffic through anonymizing services to obscure their real location and evade IP-based security controls. With 3 different attack patterns detected, this IP exhibits behavior characteristic of advanced automated scanning frameworks. Netherlands currently accounts for 106 blocked IPs in our database, making it a significant source of malicious traffic. At 255/100, this is an extremely high-risk address. All traffic should be considered hostile.
This IP is associated with a VPN or proxy service. Attackers frequently route their traffic through anonymizing services to obscure their true location. This makes attribution more challenging but the malicious behavior patterns remain detectable.
Analyzing User-Agent strings reveals automated tools masquerading as legitimate browsers. Inconsistencies between claimed browser capabilities and actual behavior, impossible version combinations, and known scanner signatures help identify malicious clients.
Advanced techniques enable threat detection while minimizing privacy impact. Encrypted DNS, differential privacy in analytics, and federated learning for threat models allow effective security monitoring without unnecessary surveillance of legitimate user behavior.