
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| UA bot: python | Known bot/crawler User-Agent detected | +40 | |
| UA changed for same IP | Multiple User-Agents — bot rotation technique | +25 | |
| Danger strong hits: 9 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 9 | Medium-risk: admin panels, config files | +60 |
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 36.77.28.170: 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.
36.77.28.170 has been assigned a threat score of 225/100 (Critical). With this rating, the IP falls into the critical severity bracket — among the most dangerous addresses in our monitoring database.
The following attack categories were identified:
36.77.28.170 is registered in Kediri, Indonesia, operating on the network of PT. Telekomunikasi Indonesia. This IP first appeared in our threat feeds after triggering multiple behavioral detection signatures. Over a period of 1 days, this IP generated 1 malicious requests, averaging approximately 1 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. With 101 flagged addresses, Indonesia represents a significant presence in our threat database. With a threat score of 225/100, this IP is among the most dangerous addresses in our database. Immediate and complete blocking is strongly recommended.
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.
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.