ABUSE.MOM
THREAT REPORT

IP Threat Report
20.226.85.132

ABUSE.MOM — BEHAVE OR GET EXPOSED

Generated: 2026-05-30 06:12:23
First seen: 2026-05-25 16:48:50
Last seen: 2026-05-30 05:59:08
280

⛔ Verdict: BLOCK

This IP address has been classified as a source of malicious automated activity. Threat score: 280/100. Total malicious requests observed: 1304.

BURSTDANGER_PATHRATIO_404REDIRECT_PROBEUA_SUS
01

Geolocation & Classification

IP Address
20.226.85.132
Type
Hosting
Country
🇧🇷 Brazil
City
São Paulo
ISP
Microsoft Corporation
Organization
Microsoft Azure Cloud (brazilsouth)
Autonomous System
AS8075 Microsoft Corporation
Hit Count
1304
02

Detection Signatures

SignatureDescriptionPointsSeverity
404 ratio 40-60%Majority of requests returned 404 — enumeration+15
Burst 13/2sAbnormally fast request rate — automated scanning+35
Burst 14/2sAbnormally fast request rate — automated scanning+35
Burst 44/10sAbnormally fast request rate — automated scanning+35
Burst 47/10sAbnormally fast request rate — automated scanning+35
Burst 48/10sAbnormally fast request rate — automated scanning+35
Burst 49/10sAbnormally fast request rate — automated scanning+35
Danger medium hits: 158Medium-risk: admin panels, config files+60
Danger medium hits: 160Medium-risk: admin panels, config files+60
Danger medium hits: 240Medium-risk: admin panels, config files+60
Danger medium hits: 84Medium-risk: admin panels, config files+60
Danger strong hits: 12High-risk paths: shells, RCE vectors, exploits+100
Danger strong hits: 4High-risk paths: shells, RCE vectors, exploits+100
Danger strong hits: 6High-risk paths: shells, RCE vectors, exploits+100
Danger strong hits: 9High-risk paths: shells, RCE vectors, exploits+100
Probe 302→404Behavioral anomaly detected by automated analysis+20
UA suspiciousBehavioral anomaly detected by automated analysis+15
Σ = 900
03

Observed Activity

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

[redacted]
GET
/
200
[redacted]
GET
/page
200
Requests shown: 2 · HTTP 404: 0 · Dangerous patterns: 0

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

04

Timeline

2026-05-25 16:48:50
First malicious request detected
IP entered monitoring from server access logs
During observation
Multiple detection signatures triggered
404 ratio 40-60% (+15), Burst 13/2s (+35), Burst 14/2s (+35)
2026-05-30 05:59:08
Last malicious request observed
Total score reached: 280/100
Next cycle
IP blocked — all subsequent requests denied (HTTP 403)
Added to blocklist automatically
05

Network Provider

Microsoft Corporation
AS8075 · 🇧🇷 Brazil
06

Recommendations

Actions taken & recommended

  • IP 20.226.85.132 is blocked at application level (HTTP 403)
  • Consider blocking at firewall level (iptables/CSF) to reduce server load
  • Report abuse to the network provider via their abuse contact
  • Ensure sensitive files (.env, .git, backups) are not accessible from the web

🔎 Directory Scan Defense

IP 20.226.85.132 is enumerating directories. Configure fail2ban apache-404 jail after 10+ 404 errors. Disable directory listings. Normalize all 404 responses.

🌊 Traffic Flood Defense

IP 20.226.85.132 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 20.226.85.132: maintain blocklist of known malicious UA strings, require consistent UA across sessions, implement TLS fingerprinting.

09

Blacklist Status (DNSBL)

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

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

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

10

Threat Analysis

20.226.85.132 has been assigned a threat score of 280/100 (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:

Path EnumerationRequest FloodingUser-Agent Anomaly

📊 Threat Analysis

IP address 20.226.85.132 has been traced to São Paulo, Brazil, operating on the network of Microsoft Corporation. Our threat detection systems have flagged this address based on observed malicious behavior patterns. During its 4-day observation window, we recorded 1,304 hostile requests from this IP — roughly 326 per day on average. Operating from datacenter infrastructure, this IP is typical of addresses used in organized attack operations. Cloud and VPS providers are commonly exploited as launching platforms for automated scanning. The diversity of 3 separate attack methods suggests a comprehensive attack toolkit — likely an automated scanner that tests for vulnerabilities across multiple categories. Our records show 12 malicious IPs originating from Brazil, positioning it as a notable contributor to global threat activity. A score of 280/100 places this address in the top tier of severity. Block and investigate any historical connections.

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.

11

Related Threats

🇧🇷 Top threats from Brazil

20.206.78.203 (283)20.226.17.188 (280)4.228.184.113 (280)20.206.65.148 (280)4.228.97.66 (280)View all →

🏢 Same network: AS8075

74.241.249.229 (340)20.151.111.128 (308)4.182.24.88 (285)20.65.61.3 (283)4.205.39.97 (283)View all →
12

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.

🔍 Check Any IP Address

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