ABUSE.MOM
THREAT REPORT

IP Threat Report
47.11.6.4

ABUSE.MOM — BEHAVE OR GET EXPOSED

Generated: 2026-05-27 06:47:17
First seen: 2026-04-24 11:00:07
Last seen: 2026-04-24 11:00:07
103

⛔ Verdict: BLOCK

This IP address has been classified as a source of malicious automated activity. Threat score: 103/100. Total malicious requests observed: 1.

DANGER_PATHMETHOD
01

Geolocation & Classification

IP Address
47.11.6.4
Type
Residential
Country
🇮🇳 India
City
Nagpur
ISP
Reliance Jio Infocomm Limited
Organization
Reliance Jio infocomm ltd
Autonomous System
AS55836 Reliance Jio Infocomm Limited
Hit Count
1
02

Detection Signatures

SignatureDescriptionPointsSeverity
Danger strong hits: 3High-risk paths: shells, RCE vectors, exploits+75
Danger medium hits: 2Medium-risk: admin panels, config files+20
POST requests presentBehavioral anomaly detected by automated analysis+8
Σ = 103
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

2026-04-24 11:00:07
First malicious request detected
IP entered monitoring from server access logs
During observation
Multiple detection signatures triggered
Danger strong hits: 3 (+75), Danger medium hits: 2 (+20), POST requests present (+8)
2026-04-24 11:00:07
Last malicious request observed
Total score reached: 103/100
Next cycle
IP blocked — all subsequent requests denied (HTTP 403)
Added to blocklist automatically
05

Network Provider

Reliance Jio Infocomm Limited
AS55836 · 🇮🇳 India
06

Recommendations

Actions taken & recommended

  • IP 47.11.6.4 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 47.11.6.0/24
  • Report abuse to the network provider via their abuse contact
  • Ensure sensitive files (.env, .git, backups) are not accessible from the web

⚙️ General Security

Add 47.11.6.4 to your firewall blocklist. Review logs for successful connections. Enable comprehensive logging on all public-facing services.

07

Neighbors in 47.11.6.0/24

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
ix.dnsbl.manitu.net
✓ Clean
zen.spamhaus.org
✓ Clean
bl.spamcop.net
✓ Clean
dnsbl.sorbs.net
✓ Clean
b.barracudacentral.org
✓ Clean
psbl.surriel.com
✓ Clean
truncate.gbudb.net
✓ Clean
dnsbl-1.uceprotect.net

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

10

Threat Analysis

47.11.6.4 has been assigned a threat score of 103/100 (Critical). With this rating, the IP falls into the critical severity bracket — among the most dangerous addresses in our monitoring database.

📊 Threat Analysis

Our monitoring infrastructure has identified 47.11.6.4, geolocated to Nagpur, India, operating on the network of Reliance Jio Infocomm Limited, as a source of suspicious network activity. Over a period of 1 days, this IP generated 1 malicious requests, averaging approximately 1 requests per day. This residential IP is likely a compromised consumer device. Home routers and IoT equipment with default credentials are prime targets for botnet operators. With 202 flagged addresses, India represents a significant presence in our threat database. At 103/100, this is an extremely high-risk address. All traffic should be considered hostile.

This IP is classified as residential, suggesting it may belong to a compromised home device, IoT botnet member, or an infected personal computer. Residential IPs involved in attacks often indicate malware infection without the owner's knowledge.

11

Related Threats

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🏢 Same network: AS55836

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12

Security Intelligence

💡 Vulnerability Scanning Explained

Vulnerability scanning is the automated process of probing web applications for known weaknesses. Attackers use tools like Nuclei, Nikto, and ZAP to test thousands of hosts per hour, looking for exposed configuration files, outdated software, and default credentials.

💡 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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