
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.
Add 193.36.224.194 to your firewall blocklist. Review logs for successful connections. Enable comprehensive logging on all public-facing services.
Other blocked IPs from the same /24 subnet — indicates systematic abuse from this network range.
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.
193.36.224.194 has been assigned a threat score of 100/100 (Critical). This represents a critical risk level. Our detection systems have flagged multiple high-confidence indicators of malicious intent from this address.
Threat intelligence analysis has linked 193.36.224.194 to malicious activity originating from Miami, United States, operating on the network of F.N.S. HOLDINGS LIMITED. The address has been under observation since its initial detection. The address has been active for 6 days in our monitoring system, producing 330 flagged requests at a rate of ~55/day. Our records show 151 malicious IPs originating from United States, positioning it as a significant contributor to global threat activity. A score of 100/100 places this address in the top tier of severity. Block and investigate any historical connections.
Mobile malware reaches devices through unofficial app stores, malicious links, and even occasionally through official stores using obfuscation techniques. Banking trojans, spyware, and ransomware variants specifically designed for mobile platforms continue to proliferate.
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.