
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Burst 17/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 9/2s | 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.
IP 178.72.67.80 is generating excessive traffic. Limit connections per source IP. Enable geographic blocking if traffic from this region is unexpected.
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.
178.72.67.80 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:
Our monitoring infrastructure has identified 178.72.67.80, geolocated to Yekaterinburg, Russia, operating on the network of TNGS-SOUTH network, as a source of suspicious network activity. Our sensors captured 70 malicious requests from this address across a 1-day span, reflecting a sustained attack cadence of ~70 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. The IP is engaged in request flooding, sending traffic at rates designed to exhaust server capacity. With 116 flagged addresses, Russia represents a significant presence in our threat database. A threat score of 80/100 places this IP in the high-risk category. Blocking at the firewall level is recommended.
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.
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.