
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Danger medium hits: 4 | Medium-risk: admin panels, config files | +40 | |
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| Probe pattern 302->404 same path | Behavioral anomaly detected by automated analysis | +20 | |
| Danger medium hits: 2 | Medium-risk: admin panels, config files | +20 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Block scanning from 203.106.68.121: rate-limit 404 responses per IP, deploy a honeypot 404 page, ensure no backup files are web-accessible.
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.
203.106.68.121 has been assigned a threat score of 75/100 (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:
Threat intelligence analysis has linked 203.106.68.121 to malicious activity originating from Shah Alam, MY, operating on the network of TMnet. The address has been under observation since its initial detection. Our sensors captured 2 malicious requests from this address across a 7-day span, reflecting a sustained attack cadence of ~0.3 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. Active path scanning has been detected — this IP probes for hundreds of common file and directory names. With 124 flagged addresses, MY represents a significant presence in our threat database. At 75/100, 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.
Machine learning models analyze vast amounts of network traffic to identify attack patterns invisible to rule-based systems. Supervised models classify known attack types while unsupervised models detect anomalies that may indicate novel threats.