
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| Danger medium hits: 4 | Medium-risk: admin panels, config files | +40 | |
| Foreign referer | Referer from unrelated external domain | +10 |
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 165.22.161.41: 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.
165.22.161.41 has been assigned a threat score of 65/100 (High). At this threat level, the IP is considered high risk. Firewall rules should be updated to deny traffic from this source.
The following attack categories were identified:
165.22.161.41 is registered in Santa Clara, United States, operating on the network of DigitalOcean, LLC. This IP first appeared in our threat feeds after triggering multiple behavioral detection signatures. The address has been active for 4 days in our monitoring system, producing 212 flagged requests at a rate of ~53/day. This address belongs to a datacenter or cloud hosting provider. Hosting IPs are frequently leveraged by threat actors who rent cheap VPS instances specifically for conducting attacks. The IP exhibits directory enumeration behavior, systematically requesting non-existent paths to discover hidden files and misconfigured resources. Our records show 118 malicious IPs originating from United States, positioning it as a significant contributor to global threat activity. At 65/100, this IP presents a meaningful threat. Implement rate limiting with escalation to blocking.
This IP belongs to a hosting or data center provider. Malicious traffic from hosting infrastructure often originates from compromised VPS instances, rented servers used for scanning campaigns, or abused free-tier cloud accounts. Hosting providers typically respond to abuse reports within 24-72 hours.
Credential stuffing uses stolen username-password pairs from data breaches to attempt logins across many websites. Since users frequently reuse passwords, these automated attacks achieve success rates of 0.1-2%, which translates to thousands of compromised accounts from millions of attempts.
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.