ABUSE.MOM
THREAT REPORT

IP Threat Report
209.87.169.55

ABUSE.MOM — BEHAVE OR GET EXPOSED

Generated: 2026-05-30 11:10:06
First seen: 2026-03-06 23:00:04
Last seen: 2026-05-30 11:05:55
75

⛔ Verdict: BLOCK

This IP address has been classified as a source of malicious automated activity. Threat score: 75/100. Total malicious requests observed: 240.

DANGER_PATHRATIO_404UA_SUS
01

Geolocation & Classification

IP Address
209.87.169.55
Type
Residential
Country
🇺🇸 United States
City
Jersey City
ISP
Active Data
Organization
Unknown
Autonomous System
AS62240 Clouvider
Hit Count
240
02

Detection Signatures

SignatureDescriptionPointsSeverity
404 ratio >= 60%Majority of requests returned 404 — enumeration+25
Danger medium hits: 1Medium-risk: admin panels, config files+10
Danger strong hits: 1High-risk paths: shells, RCE vectors, exploits+25
UA suspicious (short/empty)Behavioral anomaly detected by automated analysis+15
Σ = 75
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

2026-03-06 23:00:04
First malicious request detected
IP entered monitoring from server access logs
During observation
Multiple detection signatures triggered
404 ratio >= 60% (+25), Danger medium hits: 1 (+10), Danger strong hits: 1 (+25)
2026-05-30 11:05:55
Last malicious request observed
Total score reached: 75/100
Next cycle
IP blocked — all subsequent requests denied (HTTP 403)
Added to blocklist automatically
05

Network Provider

Active Data
AS62240 · 🇺🇸 United States
06

Recommendations

Actions taken & recommended

  • IP 209.87.169.55 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 209.87.169.0/24
  • 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 209.87.169.55 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 209.87.169.55: maintain blocklist of known malicious UA strings, require consistent UA across sessions, implement TLS fingerprinting.

07

Neighbors in 209.87.169.0/24

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
Spamhaus ZEN

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

10

Threat Analysis

209.87.169.55 has been assigned a threat score of 75/100 (High). This classifies it as a high-severity threat. Proactive blocking is recommended for sensitive infrastructure.

The following attack categories were identified:

Path EnumerationUser-Agent Anomaly

📊 Threat Analysis

209.87.169.55 is registered in Jersey City, United States, operating on the network of Active Data. This IP first appeared in our threat feeds after triggering multiple behavioral detection signatures. Over a period of 84 days, this IP generated 240 malicious requests, averaging approximately 2.9 requests per day. Operating from a residential network, this IP may represent a compromised home gateway or IoT device that has been drafted into a larger attack infrastructure. Two attack patterns were identified (Path Enumeration and User-Agent Anomaly), suggesting a semi-automated campaign that targets multiple vulnerabilities. United States currently accounts for 217 blocked IPs in our database, making it a significant source of malicious traffic. At 75/100, 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

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🏢 Same network: AS62240

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12

Security Intelligence

💡 API Abuse and Enumeration

Modern attacks increasingly target APIs rather than traditional web interfaces. Attackers enumerate endpoints, test for broken authentication, and exploit excessive data exposure. API attacks are harder to detect as they mimic legitimate programmatic access patterns.

💡 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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