
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.
Block 165.232.109.26 at the network perimeter. Implement defense-in-depth combining IP blocking with application-layer protections.
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.
165.232.109.26 has been assigned a threat score of 120/100 (Critical). This represents a critical risk level. Our detection systems have flagged multiple high-confidence indicators of malicious intent from this address.
IP address 165.232.109.26 has been traced to Slough, United Kingdom, operating on the network of DigitalOcean, LLC. Our threat detection systems have flagged this address based on observed malicious behavior patterns. During its 12-day observation window, we recorded 1,000 hostile requests from this IP — roughly 83.3 per day on average. Our records show 102 malicious IPs originating from United Kingdom, positioning it as a significant contributor to global threat activity. With a threat score of 120/100, this IP is among the most dangerous addresses in our database. Immediate and complete blocking is strongly recommended.
Deepfake audio and video enable convincing impersonation of executives and trusted individuals. Real-time voice cloning has been used in successful fraud campaigns, adding a new dimension to social engineering that traditional security training does not address.
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.