ABUSE.MOM
THREAT REPORT

IP Threat Report
5.3.221.235

ABUSE.MOM — BEHAVE OR GET EXPOSED

Generated: 2026-05-22 14:35:02
First seen: 2026-03-05 18:32:47
Last seen: 2026-03-05 18:32:47
70

⛔ Verdict: BLOCK

This IP address has been classified as a source of malicious automated activity. Threat score: 70/100. Total malicious requests observed: 1.

FORM_SPAM
01

Geolocation & Classification

IP Address
5.3.221.235
Type
Residential
Country
🇷🇺 Russia
City
Nizhniy Novgorod
ISP
JSC "ER-Telecom Holding" Nizhny Novgorod branch
Organization
JSC "ER-Telecom Holding" Nizhny Novgorod
Autonomous System
AS42682 JSC ER-Telecom Holding
Hit Count
1
02

Detection Signatures

SignatureDescriptionPointsSeverity
Form spam: no_js_checkSpam/malware keywords in request content+0
Σ = 0
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

2026-03-05 18:32:47
First malicious request detected
IP entered monitoring from server access logs
During observation
Multiple detection signatures triggered
Form spam: no_js_check
2026-03-05 18:32:47
Last malicious request observed
Total score reached: 70/100
Next cycle
IP blocked — all subsequent requests denied (HTTP 403)
Added to blocklist automatically
05

Network Provider

JSC "ER-Telecom Holding" Nizhny Novgorod branch
AS42682 · 🇷🇺 Russia
06

Recommendations

Actions taken & recommended

  • IP 5.3.221.235 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 5.3.221.0/24
  • Report abuse to the network provider via their abuse contact
  • Ensure sensitive files (.env, .git, backups) are not accessible from the web

📧 Spam Protection

Enable CAPTCHA on all public forms. Add honeypot fields. Rate-limit submissions to 3 per minute per IP. Deploy Akismet or CleanTalk.

07

Neighbors in 5.3.221.0/24

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.

⛔ LISTED
Spamhaus ZEN

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

10

Threat Analysis

5.3.221.235 has been assigned a threat score of 70/100 (High). At this threat level, the IP is considered high risk. Firewall rules should be updated to deny traffic from this source.

📊 Threat Analysis

The address 5.3.221.235 originates from Nizhniy Novgorod, Russia, operating on the network of JSC "ER-Telecom Holding" Nizhny Novgorod branch. It was identified through automated analysis of incoming network traffic across monitored endpoints. The address has been active for 1 days in our monitoring system, producing 1 flagged requests at a rate of ~1/day. The address is classified as residential, meaning it likely belongs to an end-user ISP connection. Malicious activity from residential IPs typically indicates device compromise or botnet membership. Our records show 179 malicious IPs originating from Russia, positioning it as a significant contributor to global threat activity. At 70/100, this IP warrants immediate defensive action.

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

🇷🇺 Top threats from Russia

157.22.102.172 (313)178.130.54.159 (288)95.182.125.201 (265)72.56.191.6 (265)91.240.87.225 (263)View all →

🏢 Same network: AS42682

5.3.217.81 (70)5.3.217.60 (70)5.3.221.123 (70)5.3.222.1 (70)5.3.216.129 (70)View all →
12

Security Intelligence

💡 SQL Injection Campaigns

SQL injection remains one of the most common web attack vectors. Attackers inject malicious SQL code through input fields to extract database contents, modify data, or gain administrative access. Automated scanners test for SQLi vulnerabilities at massive scale.

💡 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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