
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| Burst 13/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 14/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 19/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 21/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 13 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 14 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 19 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 21 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger medium hits: 4 | Medium-risk: admin panels, config files | +40 | |
| Danger medium hits: 6 | Medium-risk: admin panels, config files | +60 | |
| Foreign referer | Referer from unrelated external domain | +10 | |
| Foreign referer seen | Referer from unrelated external domain | +10 | |
| Probe 302→404 | Behavioral anomaly detected by automated analysis | +20 | |
| Probe pattern 302->404 same path | Behavioral anomaly detected by automated analysis | +20 | |
| UA changed | Multiple User-Agents — bot rotation technique | +25 | |
| UA changed for same IP | Multiple User-Agents — bot rotation technique | +25 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Block scanning from 34.72.216.33: rate-limit 404 responses per IP, deploy a honeypot 404 page, ensure no backup files are web-accessible.
IP 34.72.216.33 is generating excessive traffic. Limit connections per source IP. Enable geographic blocking if traffic from this region is unexpected.
IP 34.72.216.33 shows suspicious UA behavior. Block empty User-Agent requests. Implement JavaScript-based bot detection for sensitive endpoints.
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.
34.72.216.33 has been assigned a threat score of 180/100 (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 34.72.216.33, geolocated to Council Bluffs, United States, operating on the network of Google LLC, as a source of suspicious network activity. Our sensors captured 435 malicious requests from this address across a 6-day span, reflecting a sustained attack cadence of ~72.5 requests per day. The IP is classified as hosting/datacenter infrastructure, commonly associated with rented servers used for automated attack campaigns, botnet command-and-control, or vulnerability scanning at scale. The combination of 3 distinct attack vectors indicates a sophisticated, multi-pronged threat actor deploying automated tools that probe multiple attack surfaces simultaneously. With 140 flagged addresses, United States represents a significant presence in our threat database. A score of 180/100 places this address in the top tier of severity. Block and investigate any historical connections.
This IP belongs to a hosting or data center provider. Malicious traffic from hosting infrastructure often originates from compromised VPS instances, rented servers used for scanning campaigns, or abused free-tier cloud accounts. Hosting providers typically respond to abuse reports within 24-72 hours.
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.
Analyzing network flows (NetFlow, sFlow, IPFIX) provides visibility into traffic patterns without inspecting packet contents. Flow data reveals scanning activity, data exfiltration, lateral movement, and command-and-control channels at scale.