
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| 404 ratio >= 60% | Majority of requests returned 404 — enumeration | +25 | |
| Danger medium hits: 1 | Medium-risk: admin panels, config files | +10 | |
| Danger strong hits: 1 | High-risk paths: shells, RCE vectors, exploits | +25 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
IP 45.3.43.59 is enumerating directories. Configure fail2ban apache-404 jail after 10+ 404 errors. Disable directory listings. Normalize all 404 responses.
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.
45.3.43.59 has been assigned a threat score of 60/100 (High). This classifies it as a high-severity threat. Proactive blocking is recommended for sensitive infrastructure.
The following attack categories were identified:
The address 45.3.43.59 originates from Berlin, Germany, operating on the network of 3xK Tech GmbH. It was identified through automated analysis of incoming network traffic across monitored endpoints. During its 6-day observation window, we recorded 429 hostile requests from this IP — roughly 71.5 per day on average. This IP is identified as a VPN or proxy endpoint, commonly used to mask the true origin of attack traffic and bypass geographic or reputation-based blocking. The IP exhibits directory enumeration behavior, systematically requesting non-existent paths to discover hidden files and misconfigured resources. Germany currently accounts for 133 blocked IPs in our database, making it a significant source of malicious traffic. At 60/100, this IP presents a meaningful threat. Implement rate limiting with escalation to blocking.
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.
Path traversal attacks attempt to access files outside the intended directory by manipulating file path references. Attackers use sequences like ../ to reach sensitive system files such as /etc/passwd or application configuration files.
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.