
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 111.6.117.16.
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.
111.6.117.16 has been assigned a threat score of 80/100 (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:
Network traffic from 111.6.117.16, located in Guancheng, China, operating on the network of China Mobile communications corporation, has been classified as malicious by our automated threat scoring engine. During its 2-day observation window, we recorded 139 hostile requests from this IP — roughly 69.5 per day on average. 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 102 flagged addresses, China represents a significant presence in our threat database. The score of 80/100 indicates a confirmed malicious actor. Network-level blocking is appropriate.
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.
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.