
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| 404 ratio >= 60% | Majority of requests returned 404 — enumeration | +25 | |
| UA bot: curl | Known bot/crawler User-Agent detected | +40 |
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 82.206.73.133: rate-limit 404 responses per IP, deploy a honeypot 404 page, ensure no backup files are web-accessible.
IP 82.206.73.133 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.
82.206.73.133 has been assigned a threat score of 65/100 (High). This classifies it as a high-severity threat. Proactive blocking is recommended for sensitive infrastructure.
The following attack categories were identified:
Network traffic from 82.206.73.133, located in Baden, Switzerland, operating on the network of M247 Europe SRL, has been classified as malicious by our automated threat scoring engine. The address has been active for 1 days in our monitoring system, producing 43 flagged requests at a rate of ~43/day. Operating from datacenter infrastructure, this IP is typical of addresses used in organized attack operations. Cloud and VPS providers are commonly exploited as launching platforms for automated scanning. Two attack patterns were identified (Path Enumeration and User-Agent Anomaly), suggesting a semi-automated campaign that targets multiple vulnerabilities. The score of 65/100 warrants active monitoring and rate-limiting. Full blocking is advisable for sensitive systems.
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.
Analyzing User-Agent strings reveals automated tools masquerading as legitimate browsers. Inconsistencies between claimed browser capabilities and actual behavior, impossible version combinations, and known scanner signatures help identify malicious clients.
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.