
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: 338 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 742 | Medium-risk: admin panels, config files | +60 | |
| Burst: 38 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 120 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger strong hits: 127 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 50 | Medium-risk: admin panels, config files | +60 | |
| Burst: 50 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 150 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger strong hits: 378 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 970 | Medium-risk: admin panels, config files | +60 | |
| Burst: 57 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 200 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger strong hits: 356 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 629 | Medium-risk: admin panels, config files | +60 | |
| Burst: 55 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 197 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.
IP 45.86.200.157 shows suspicious UA behavior. Block empty User-Agent requests. Implement JavaScript-based bot detection for sensitive endpoints.
IP 45.86.200.157 is generating excessive traffic. Limit connections per source IP. Enable geographic blocking if traffic from this region is unexpected.
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.
45.86.200.157 has been assigned a threat score of 255/100 (Critical). This is a critical-level threat. Systems administrators should treat this IP as hostile and block all inbound connections without exception.
The following attack categories were identified:
Our monitoring infrastructure has identified 45.86.200.157, geolocated to The Hague, Netherlands, operating on the network of F.N.S. HOLDINGS LIMITED, as a source of suspicious network activity. The address has been active for 5 days in our monitoring system, producing 5 flagged requests at a rate of ~1/day. This IP is identified as a VPN or proxy endpoint, commonly used to mask the true origin of attack traffic and bypass geographic or reputation-based blocking. Two attack patterns were identified (User-Agent Anomaly and Request Flooding), suggesting a semi-automated campaign that targets multiple vulnerabilities. Netherlands currently accounts for 106 blocked IPs in our database, making it a significant source of malicious traffic. With a threat score of 255/100, this IP is among the most dangerous addresses in our database. Immediate and complete blocking is strongly recommended.
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.
Examining HTTP headers beyond User-Agent reveals attack tools and automated scripts. Missing standard headers, unusual ordering, non-standard values, and inconsistencies with claimed client identity all serve as reliable detection signals.
WAFs inspect HTTP traffic to block common attacks but require careful tuning. Overly aggressive rules cause false positives while permissive configurations miss attacks. Modern WAFs combine signature matching with behavioral analysis and machine learning.