
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| UA bot: python | Known bot/crawler User-Agent detected | +40 | |
| 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.
Address UA spoofing from 154.93.45.21: 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.
154.93.45.21 has been assigned a threat score of 65/100 (High). This score indicates high threat severity. The IP has shown clear patterns of malicious behavior that warrant immediate defensive measures.
The following attack categories were identified:
IP address 154.93.45.21 has been traced to Cairo, EG, operating on the network of Kaopu Cloud HK Limited. Our threat detection systems have flagged this address based on observed malicious behavior patterns. Our sensors captured 233 malicious requests from this address across a 4-day span, reflecting a sustained attack cadence of ~58.3 requests per day. This is a residential IP address, suggesting a compromised home device such as a router, smart appliance, or infected workstation participating in a botnet. The IP exhibits User-Agent manipulation, switching between different browser identities or sending empty headers. Our records show 80 malicious IPs originating from EG, positioning it as a notable contributor to global threat activity. The score of 65/100 warrants active monitoring and rate-limiting. Full blocking is advisable for sensitive systems.
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.
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.
Signature-based detection matches known attack patterns but misses novel threats. Behavioral analysis identifies anomalies in request patterns, timing, and volume, catching zero-day attacks that signatures cannot recognize.