
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| Danger medium hits: 3 | Medium-risk: admin panels, config files | +30 | |
| Foreign referer | Referer from unrelated external domain | +10 | |
| Probe 302→404 | 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 201.150.118.82 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.
201.150.118.82 has been assigned a threat score of 75/100 (High). This classifies it as a high-severity threat. Proactive blocking is recommended for sensitive infrastructure.
The following attack categories were identified:
IP address 201.150.118.82 has been traced to Tetepango, Mexico, operating on the network of Hulux Telecomunicaciones. Our threat detection systems have flagged this address based on observed malicious behavior patterns. During its 4-day observation window, we recorded 126 hostile requests from this IP — roughly 31.5 per day on average. Operating from a residential network, this IP may represent a compromised home gateway or IoT device that has been drafted into a larger attack infrastructure. The IP exhibits directory enumeration behavior, systematically requesting non-existent paths to discover hidden files and misconfigured resources. Our records show 288 malicious IPs originating from Mexico, positioning it as a significant contributor to global threat activity. 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.
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.
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.