
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Directory Scan | Behavioral anomaly detected by automated analysis | +0 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Block 130.194.71.113 at the network perimeter. Implement defense-in-depth combining IP blocking with application-layer protections.
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.
130.194.71.113 has been assigned a threat score of 85/100 (Critical). This is a critical-level threat. Systems administrators should treat this IP as hostile and block all inbound connections without exception.
IP address 130.194.71.113 has been traced to Melbourne, Australia, operating on the network of Monash University. Our threat detection systems have flagged this address based on observed malicious behavior patterns. Our sensors captured 126 malicious requests from this address across a 1-day span, reflecting a sustained attack cadence of ~126 requests per day. With 101 flagged addresses, Australia represents a significant presence in our threat database. A threat score of 85/100 places this IP in the high-risk category. Blocking at the firewall level is recommended.
Artificial intelligence enables more convincing phishing content, faster vulnerability discovery, and adaptive attack strategies that learn from defensive responses. AI-generated social engineering and automated exploit development represent growing threats.
GraphQL APIs introduce specific vulnerabilities including introspection information disclosure, query complexity attacks, batching abuse, and authorization bypass through nested queries. Depth limiting, cost analysis, and field-level authorization address these GraphQL-specific threats.