
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Directory Scan | Behavioral anomaly detected by automated analysis | +0 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Add 216.73.163.190 to your firewall blocklist. Review logs for successful connections. Enable comprehensive logging on all public-facing services.
Other blocked IPs from the same /24 subnet — indicates systematic abuse from this network range.
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.
216.73.163.190 has been assigned a threat score of 100/100 (Critical). This is a critical-level threat. Systems administrators should treat this IP as hostile and block all inbound connections without exception.
Our monitoring infrastructure has identified 216.73.163.190, geolocated to San Francisco, United States, operating on the network of Bandito Networks, as a source of suspicious network activity. Our sensors captured 238 malicious requests from this address across a 6-day span, reflecting a sustained attack cadence of ~39.7 requests per day. With 151 flagged addresses, United States represents a significant presence in our threat database. A score of 100/100 places this address in the top tier of severity. Block and investigate any historical connections.
Tor exit nodes are publicly listed but constantly rotating. While Tor serves essential privacy functions for journalists and activists, it is also used to anonymize attacks. Effective security policies differentiate between blocking and monitoring Tor traffic.
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.