
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| UA changed for same IP | Multiple User-Agents — bot rotation technique | +25 | |
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| Burst: 5 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Foreign referer seen | Referer from unrelated external domain | +10 | |
| Danger strong hits: 2 | High-risk paths: shells, RCE vectors, exploits | +50 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Address UA spoofing from 178.208.67.38: maintain blocklist of known malicious UA strings, require consistent UA across sessions, implement TLS fingerprinting.
Block scanning from 178.208.67.38: rate-limit 404 responses per IP, deploy a honeypot 404 page, ensure no backup files are web-accessible.
Implement limit_req_zone in nginx. Deploy CDN with DDoS protection. Configure SYN cookies and connection tracking to throttle 178.208.67.38.
Network reconnaissance data from Shodan. Open ports may indicate running services, misconfigurations, or potential attack surfaces.
| Port | Service | Risk | Description |
|---|---|---|---|
| 21 | FTP | Medium | File Transfer Protocol — often targeted for anonymous login attacks |
| 22 | SSH | Low | Secure Shell — common brute force target for remote access |
| 80 | HTTP | Low | HTTP web server — standard web traffic |
| 81 | Unknown | Low | Service on port 81 |
| 161 | Unknown | Low | Service on port 161 |
| 873 | Unknown | Low | Service on port 873 |
| 3306 | MySQL | High | MySQL database — should never be exposed to the internet |
⚠️ 2 high-risk ports detected on 178.208.67.38. These services should not be publicly accessible without strict firewall rules.
| CVE ID | Link |
|---|---|
| CVE-2023-51385 | NVD → |
| CVE-2023-48795 | NVD → |
| CVE-2016-20012 | NVD → |
| CVE-2008-3844 | NVD → |
| CVE-2024-6387 | NVD → |
| CVE-2023-51767 | NVD → |
| CVE-2021-36368 | NVD → |
| CVE-2021-41617 | NVD → |
| CVE-2023-38408 | NVD → |
| CVE-2007-2768 | NVD → |
| CVE-2025-32728 | NVD → |
| CVE-2025-26465 | NVD → |
🔴 This host has 12 known CVEs associated with its exposed services. This volume strongly suggests severely outdated software. Review each CVE in the NVD database.
Data source: Shodan InternetDB. Scanned independently of abuse.mom.
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.
178.208.67.38 has been assigned a threat score of 85/100 (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 178.208.67.38 originates from Amsterdam, Netherlands, operating on the network of MCHOST. It was identified through automated analysis of incoming network traffic across monitored endpoints. The address has been active for 8 days in our monitoring system, producing 2 flagged requests at a rate of ~0.3/day. The IP is classified as hosting/datacenter infrastructure, commonly associated with rented servers used for automated attack campaigns, botnet command-and-control, or vulnerability scanning at scale. With 3 different attack patterns detected, this IP exhibits behavior characteristic of advanced automated scanning frameworks. With 101 flagged addresses, Netherlands represents a significant presence in our threat database. At 85/100, this IP warrants immediate defensive action.
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.
TLS fingerprinting creates unique identifiers based on how clients negotiate encrypted connections. The JA3 and JA4 methods generate hashes from TLS ClientHello parameters, enabling identification of specific tools and malware regardless of IP address changes.
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.