
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| Danger medium hits: 5 | Medium-risk: admin panels, config files | +50 | |
| 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.
Block scanning from 178.20.215.158: 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.
178.20.215.158 has been assigned a threat score of 95/100 (Critical). This is a critical-level threat. Systems administrators should treat this IP as hostile and block all inbound connections without exception.
The following attack categories were identified:
The address 178.20.215.158 originates from Saint-Denis, France, operating on the network of Limestone Networks, Inc.. It was identified through automated analysis of incoming network traffic across monitored endpoints. Our sensors captured 28 malicious requests from this address across a 1-day span, reflecting a sustained attack cadence of ~28 requests per day. The address operates as a VPN/proxy exit node. Attackers route traffic through anonymizing services to obscure their real location and evade IP-based security controls. Active path scanning has been detected — this IP probes for hundreds of common file and directory names. With 14 flagged addresses, France represents a notable presence in our threat database. With a threat score of 95/100, this IP is among the most dangerous addresses in our database. Immediate and complete blocking is strongly recommended.
This IP is associated with a VPN or proxy service. Attackers frequently route their traffic through anonymizing services to obscure their true location. This makes attribution more challenging but the malicious behavior patterns remain detectable.
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.