
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Danger strong hits: 2 | High-risk paths: shells, RCE vectors, exploits | +50 | |
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| Probe pattern 302->404 same path | Behavioral anomaly detected by automated analysis | +20 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
IP 113.22.11.63 is enumerating directories. Configure fail2ban apache-404 jail after 10+ 404 errors. Disable directory listings. Normalize all 404 responses.
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.
113.22.11.63 has been assigned a threat score of 85/100 (Critical). This places it in the critical threat category. Immediate blocking is strongly advised across all network perimeters.
The following attack categories were identified:
Network traffic from 113.22.11.63, located in Ho Chi Minh City, Vietnam, operating on the network of FPT Telecom Company, has been classified as malicious by our automated threat scoring engine. During its 1-day observation window, we recorded 1 hostile requests from this IP — roughly 1 per day on average. The address is classified as residential, meaning it likely belongs to an end-user ISP connection. Malicious activity from residential IPs typically indicates device compromise or botnet membership. Active path scanning has been detected — this IP probes for hundreds of common file and directory names. With 147 flagged addresses, Vietnam represents a significant presence in our threat database. The score of 85/100 indicates a confirmed malicious actor. Network-level blocking is appropriate.
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.
Modern attacks increasingly target APIs rather than traditional web interfaces. Attackers enumerate endpoints, test for broken authentication, and exploit excessive data exposure. API attacks are harder to detect as they mimic legitimate programmatic access patterns.
Advanced techniques enable threat detection while minimizing privacy impact. Encrypted DNS, differential privacy in analytics, and federated learning for threat models allow effective security monitoring without unnecessary surveillance of legitimate user behavior.