
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 | |
| POST requests present | Behavioral anomaly detected by automated analysis | +8 |
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 103.84.193.221: 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.
103.84.193.221 has been assigned a threat score of 73/100 (High). This classifies it as a high-severity threat. Proactive blocking is recommended for sensitive infrastructure.
The following attack categories were identified:
The address 103.84.193.221 originates from Jakarta, Indonesia, operating on the network of PT Klik Media Netindo. It was identified through automated analysis of incoming network traffic across monitored endpoints. During its 22-day observation window, we recorded 25 hostile requests from this IP — roughly 1.1 per day on average. Operating from a residential network, this IP may represent a compromised home gateway or IoT device that has been drafted into a larger attack infrastructure. 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. A threat score of 73/100 places this IP in the high-risk category. Blocking at the firewall level is 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.
TLS fingerprinting creates unique identifiers based on how clients negotiate encrypted connections. The JA3 and JA4 methods generate hashes from TLS ClientHello parameters, enabling identification of specific tools and malware regardless of IP address changes.
Race conditions occur when application behavior depends on the timing of concurrent operations. Attackers exploit these timing windows to bypass limits, duplicate transactions, or escalate privileges by sending carefully timed parallel requests.