ABUSE.MOM
THREAT REPORT

IP Threat Report
185.220.101.25

ABUSE.MOM — BEHAVE OR GET EXPOSED

Generated: 2026-05-27 09:02:10
First seen: 2026-05-27 01:39:19
Last seen: 2026-05-27 08:39:07
73

⛔ Verdict: BLOCK

This IP address has been classified as a source of malicious automated activity. Threat score: 73/100. Total malicious requests observed: 18.

BOT_UAMETHODRATIO_404REFERER
01

Geolocation & Classification

IP Address
185.220.101.25
Type
Unknown
Country
🇤🇤 ??
City
Unknown
ISP
Unknown
Organization
Unknown
Autonomous System
Unknown
Hit Count
18
02

Detection Signatures

SignatureDescriptionPointsSeverity
404 ratio 40-60%Majority of requests returned 404 — enumeration+15
Foreign refererReferer from unrelated external domain+10
POST seenBehavioral anomaly detected by automated analysis+8
UA bot: Go-http-clientKnown bot/crawler User-Agent detected+40
Σ = 73
03

Observed Activity

Reconstructed HTTP requests from server access logs. Target domains redacted for security.

[redacted]
GET
/
200
Requests shown: 1 · HTTP 404: 0 · Dangerous patterns: 0

* Typical request patterns for detected signatures. Actual target domains are redacted.

04

Timeline

2026-05-27 01:39:19
First malicious request detected
IP entered monitoring from server access logs
During observation
Multiple detection signatures triggered
404 ratio 40-60% (+15), Foreign referer (+10), POST seen (+8)
2026-05-27 08:39:07
Last malicious request observed
Total score reached: 73/100
Next cycle
IP blocked — all subsequent requests denied (HTTP 403)
Added to blocklist automatically
06

Recommendations

Actions taken & recommended

  • IP 185.220.101.25 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 185.220.101.0/24
  • Ensure sensitive files (.env, .git, backups) are not accessible from the web

🔎 Directory Scan Defense

IP 185.220.101.25 is enumerating directories. Configure fail2ban apache-404 jail after 10+ 404 errors. Disable directory listings. Normalize all 404 responses.

🤖 Bot Detection

Address UA spoofing from 185.220.101.25: maintain blocklist of known malicious UA strings, require consistent UA across sessions, implement TLS fingerprinting.

07

Neighbors in 185.220.101.0/24

Other blocked IPs from the same /24 subnet — indicates systematic abuse from this network range.

09

Blacklist Status (DNSBL)

This IP was checked against major DNS-based blacklists used by mail servers and firewalls worldwide.

✓ Clean
spam.dnsbl.sorbs.net
✓ Clean
bl.spamcop.net
✓ Clean
zen.spamhaus.org
✓ Clean
b.barracudacentral.org
✓ Clean
psbl.surriel.com
✓ Clean
cbl.abuseat.org
✓ Clean
dnsbl.dronebl.org

Checked: Spamhaus, SpamCop, Barracuda, SORBS, CBL, UCEProtect. Results may change over time.

10

Threat Analysis

185.220.101.25 has been assigned a threat score of 73/100 (High). The IP is rated as a high-level threat. Network administrators should implement blocking rules and monitor for any connections from this address.

The following attack categories were identified:

Path EnumerationUser-Agent Anomaly

📊 Threat Analysis

IP address 185.220.101.25 has been traced to an unknown location. Our threat detection systems have flagged this address based on observed malicious behavior patterns. During its 1-day observation window, we recorded 18 hostile requests from this IP — roughly 18 per day on average. The dual attack vectors of Path Enumeration combined with User-Agent Anomaly indicate a coordinated assault rather than opportunistic scanning. The score of 73/100 indicates a confirmed malicious actor. Network-level blocking is appropriate.

11

Related Threats

🇤🇤 Top threats from ??

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12

Security Intelligence

💡 TLS Fingerprinting (JA3/JA4)

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 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.

🔍 Check Any IP Address

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