
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Burst 13/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 13/2s | Abnormally fast request rate — automated scanning | +35 | |
| Foreign referer | Referer from unrelated external domain | +10 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Implement limit_req_zone in nginx. Deploy CDN with DDoS protection. Configure SYN cookies and connection tracking to throttle 92.184.113.116.
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.
92.184.113.116 has been assigned a threat score of 80/100 (Critical). This places it in the critical threat category. Immediate blocking is strongly advised across all network perimeters.
The following attack categories were identified:
The address 92.184.113.116 originates from Orange, France, operating on the network of Orange S.A.. It was identified through automated analysis of incoming network traffic across monitored endpoints. Over a period of 2 days, this IP generated 288 malicious requests, averaging approximately 144 requests per day. This is a mobile network IP. While mobile addresses are typically shared via CGNAT, persistent malicious activity from this specific address suggests automated abuse. Rate-based attacks from this IP aim to overwhelm server resources through high-volume request flooding. With 135 flagged addresses, France represents a significant presence in our threat database. At 80/100, this IP warrants immediate defensive action.
Distributed denial of service attacks overwhelm infrastructure with traffic volume. Effective mitigation combines always-on traffic scrubbing, anycast network distribution, rate limiting, and the ability to quickly scale absorption capacity during attacks.
Signature-based detection matches known attack patterns but misses novel threats. Behavioral analysis identifies anomalies in request patterns, timing, and volume, catching zero-day attacks that signatures cannot recognize.