ABUSE.MOM
THREAT REPORT

IP Threat Report
196.247.162.170

ABUSE.MOM — BEHAVE OR GET EXPOSED

Generated: 2026-05-30 06:44:38
First seen: 2026-04-22 14:24:40
Last seen: 2026-04-22 14:24:40
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
196.247.162.170
Type
Residential
Country
🇬🇧 United Kingdom
City
City of London
ISP
Angelnet Limited
Organization
Fiber Grid
Autonomous System
AS57858 Angelnet Limited
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

2026-04-22 14:24:40
First malicious request detected
IP entered monitoring from server access logs
During observation
Multiple detection signatures triggered
Form spam: no_js_check
2026-04-22 14:24:40
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

Angelnet Limited
AS57858 · 🇬🇧 United Kingdom
06

Recommendations

Actions taken & recommended

  • IP 196.247.162.170 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

📧 Spam Protection

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

08

Open Ports & Services

Network reconnaissance data from Shodan. Open ports may indicate running services, misconfigurations, or potential attack surfaces.

OPEN PORTS (2)
PortServiceRiskDescription
80HTTPLowHTTP web server — standard web traffic
7777UnknownLowService on port 7777

Data source: Shodan InternetDB. Scanned independently of abuse.mom.

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

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

10

Threat Analysis

196.247.162.170 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

IP address 196.247.162.170 has been traced to City of London, United Kingdom, operating on the network of Angelnet Limited. Our threat detection systems have flagged this address based on observed malicious behavior patterns. Our sensors captured 1 malicious requests from this address across a 1-day span, reflecting a sustained attack cadence of ~1 requests per 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. 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

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🏢 Same network: AS57858

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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.

💡 Privacy-Preserving Threat Detection

Advanced techniques enable threat detection while minimizing privacy impact. Encrypted DNS, differential privacy in analytics, and federated learning for threat models allow effective security monitoring without unnecessary surveillance of legitimate user behavior.

🔍 Check Any IP Address

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