IP Threat Report
194.32.120.186
ABUSE.MOM — BEHAVE OR GET EXPOSED
Geolocation & Classification
Detection Signatures
| Signature | Description | Points | Severity |
|---|---|---|---|
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| Burst 13/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 45/10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger medium hits: 32 | Medium-risk: admin panels, config files | +60 | |
| Danger strong hits: 16 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Foreign referer | Referer from unrelated external domain | +10 | |
| UA bot: Go-http-client | Known bot/crawler User-Agent detected | +40 |
Observed Activity
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Timeline
Network Provider
Recommendations
Actions taken & recommended
- IP 194.32.120.186 is blocked at application level (HTTP 403)
- Consider blocking at firewall level (iptables/CSF) to reduce server load
- Other malicious IPs detected in the same /24 subnet — consider blocking 194.32.120.0/24
- Report abuse to the network provider via their abuse contact
- Ensure sensitive files (.env, .git, backups) are not accessible from the web
🔎 Path Enumeration Protection
Block scanning from 194.32.120.186: rate-limit 404 responses per IP, deploy a honeypot 404 page, ensure no backup files are web-accessible.
🌊 Traffic Flood Defense
IP 194.32.120.186 is generating excessive traffic. Limit connections per source IP. Enable geographic blocking if traffic from this region is unexpected.
🤖 Bot Detection
Address UA spoofing from 194.32.120.186: maintain blocklist of known malicious UA strings, require consistent UA across sessions, implement TLS fingerprinting.
Neighbors in 194.32.120.0/24
Other blocked IPs from the same /24 subnet — indicates systematic abuse from this network range.
Blacklist Status (DNSBL)
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.
Threat Analysis
194.32.120.186 has been assigned a threat score of 295/100 (Critical). This represents a critical risk level. Our detection systems have flagged multiple high-confidence indicators of malicious intent from this address.
The following attack categories were identified:
📊 Threat Analysis
194.32.120.186 is registered in London, United Kingdom, operating on the network of UK Dedicated Servers Limited. This IP first appeared in our threat feeds after triggering multiple behavioral detection signatures. Our sensors captured 127 malicious requests from this address across a 4-day span, reflecting a sustained attack cadence of ~31.8 requests per 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. The diversity of 3 separate attack methods suggests a comprehensive attack toolkit — likely an automated scanner that tests for vulnerabilities across multiple categories. United Kingdom currently accounts for 194 blocked IPs in our database, making it a significant source of malicious traffic. At 295/100, this is an extremely high-risk address. All traffic should be considered hostile.
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.
Related Threats
🇬🇧 Top threats from United Kingdom
45.86.203.126 (350)104.28.240.60 (340)104.28.192.67 (340)104.28.208.60 (340)194.32.120.30 (320)View all →Security Intelligence
💡 DDoS Mitigation Approaches
Distributed denial of service attacks overwhelm infrastructure with traffic volume. Effective mitigation combines always-on traffic scrubbing, anycast network distribution, rate limiting, and the ability to quickly scale absorption capacity during attacks.
💡 Machine Learning in Threat Detection
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.