
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Burst: 5 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 15 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.
Implement limit_req_zone in nginx. Deploy CDN with DDoS protection. Configure SYN cookies and connection tracking to throttle 110.177.182.12.
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.
110.177.182.12 has been assigned a threat score of 70/100 (High). The IP is rated as a high-level threat. Network administrators should implement blocking rules and monitor for any connections from this address.
The following attack categories were identified:
Network traffic from 110.177.182.12, located in Taiyuan, China, operating on the network of Chinanet, has been classified as malicious by our automated threat scoring engine. During its 1-day observation window, we recorded 1 hostile requests from this IP — roughly 1 per day on average. This residential IP is likely a compromised consumer device. Home routers and IoT equipment with default credentials are prime targets for botnet operators. Rate-based attacks from this IP aim to overwhelm server resources through high-volume request flooding. Our records show 194 malicious IPs originating from China, positioning it as a significant contributor to global threat activity. A threat score of 70/100 places this IP in the high-risk category. Blocking at the firewall level is recommended.
This IP is classified as residential, suggesting it may belong to a compromised home device, IoT botnet member, or an infected personal computer. Residential IPs involved in attacks often indicate malware infection without the owner's knowledge.
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.
Insider threats — whether malicious or negligent — account for a significant percentage of data breaches. Behavioral analytics detecting unusual access patterns, data downloads, and privilege escalation help identify insider risks before damage occurs.