
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| 404 ratio >= 60% | Majority of requests returned 404 — enumeration | +25 | |
| Burst 139/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 23/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 25/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 28/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 40/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 68/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 83/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 94/10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger medium hits: 40 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 80 | Medium-risk: admin panels, config files | +60 | |
| Danger strong hits: 241 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger strong hits: 482 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| POST seen | Behavioral anomaly detected by automated analysis | +8 | |
| UA changed | 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 34.159.182.197: rate-limit 404 responses per IP, deploy a honeypot 404 page, ensure no backup files are web-accessible.
IP 34.159.182.197 is generating excessive traffic. Limit connections per source IP. Enable geographic blocking if traffic from this region is unexpected.
IP 34.159.182.197 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.
34.159.182.197 has been assigned a threat score of 288/100 (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:
Threat intelligence analysis has linked 34.159.182.197 to malicious activity originating from Frankfurt, Germany, operating on the network of Google LLC. The address has been under observation since its initial detection. The address has been active for 1 days in our monitoring system, producing 67 flagged requests at a rate of ~67/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 diversity of 3 separate attack methods suggests a comprehensive attack toolkit — likely an automated scanner that tests for vulnerabilities across multiple categories. Germany currently accounts for 104 blocked IPs in our database, making it a significant source of malicious traffic. With a threat score of 288/100, this IP is among the most dangerous addresses in our database. Immediate and complete blocking is strongly recommended.
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.
WAFs inspect HTTP traffic to block common attacks but require careful tuning. Overly aggressive rules cause false positives while permissive configurations miss attacks. Modern WAFs combine signature matching with behavioral analysis and machine learning.