
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| UA bot: crawler | Known bot/crawler User-Agent detected | +40 | |
| 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.
Address UA spoofing from 45.18.8.219: maintain blocklist of known malicious UA strings, require consistent UA across sessions, implement TLS fingerprinting.
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.
45.18.8.219 has been assigned a threat score of 65/100 (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:
Threat intelligence analysis has linked 45.18.8.219 to malicious activity originating from Oviedo, United States, operating on the network of AT&T Enterprises, LLC. The address has been under observation since its initial detection. 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. The IP exhibits User-Agent manipulation, switching between different browser identities or sending empty headers. United States currently accounts for 164 blocked IPs in our database, making it a significant source of malicious traffic. At 65/100, this IP presents a meaningful threat. Implement rate limiting with escalation to blocking.
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.
Examining HTTP headers beyond User-Agent reveals attack tools and automated scripts. Missing standard headers, unusual ordering, non-standard values, and inconsistencies with claimed client identity all serve as reliable detection signals.
Analyzing attack patterns at the AS (Autonomous System) level reveals which networks harbor the most malicious activity. Some ASes have abuse rates orders of magnitude higher than average, indicating lax enforcement of acceptable use policies.