
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Danger medium hits: 6 | Medium-risk: admin panels, config files | +60 | |
| Foreign referer seen | Referer from unrelated external domain | +10 | |
| Danger medium hits: 4 | Medium-risk: admin panels, config files | +40 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Add 202.55.67.194 to your firewall blocklist. Review logs for successful connections. Enable comprehensive logging on all public-facing services.
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.
202.55.67.194 has been assigned a threat score of 70/100 (High). This score indicates high threat severity. The IP has shown clear patterns of malicious behavior that warrant immediate defensive measures.
202.55.67.194 is registered in Singapore, Singapore, operating on the network of Viewqwest Pte Ltd. This IP first appeared in our threat feeds after triggering multiple behavioral detection signatures. During its 35-day observation window, we recorded 2 hostile requests from this IP — roughly 0.1 per day on average. This is a residential IP address, suggesting a compromised home device such as a router, smart appliance, or infected workstation participating in a botnet. Singapore currently accounts for 101 blocked IPs in our database, making it a significant source of malicious traffic. At 70/100, this IP warrants immediate defensive action.
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.
SSRF attacks trick servers into making requests to internal resources that should not be publicly accessible. This can expose cloud metadata endpoints, internal APIs, and private network services, potentially leading to full infrastructure compromise.
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.