SCAN INCOMPLETE - LIMITED DATA COLLECTED

There were problems collecting data from this website

The website may be blocking automated browsers (bot protection)
The site may be using geo-blocking or rate limiting
Network connectivity issues may have prevented access

LIMITED DATA

Note: There were problems collecting data during this scan, and some information may be missing or incomplete. The security analysis below is based on limited information and may not be accurate. Consider trying the scan again.

Security Scan Report: madisonbkimura.pages.dev

Submitted: Dec 16, 2025, 6:45:19 AMCompleted: Dec 16, 2025, 6:46:10 AMpubliccompleted
Loading additional data...

Summary

This website contacted 4 IPs in 1 country across 1 domain to perform 3 HTTP transactions. The main domain is madisonbkimura.pages.dev and was registered NaN years ago.

Submitted URL: https://madisonbkimura.pages.dev/ooiyem-social-security-calendar-2025-gidfds/

AI Security Verdict

Safe Website

Confidence: 95%

0
Risk Score

No security concerns detected; site appears legitimate.

Safety Factors
Well‑established domain (>5 years)
No forms collecting sensitive data
No malicious Indicators of Compromise
Domain age information unavailable

Details

Page Title

Social Security Calendar 2025 - Frances C. Slater

Scan Type

public

Language

🇺🇸

English

(80% confidence)

Category

technology software

(78%)

Domain Information

The domain 'madisonbkimura.pages.dev' uses the developer-focused generic top-level domain (.dev); it also runs on subdomain 'madisonbkimura'. The second-level label 'pages' is 5 characters long containing 2 vowels alongside three consonants. Segmentation suggests 1 word: pages. No strong language cues emerged from the frequency lists.

Screenshot

Security scan screenshot of https://madisonbkimura.pages.dev/ooiyem-social-security-calendar-2025-gidfds/

Page Load Overview

13.20s
Total Load Time
3
HTTP Requests
1
Domains
N/A
Total Size

Language Analysis

Primary Language

🇺🇸English
Code: en
Confidence:80%
Script:Latin
Direction:ltr

Detection Details

Language Code:en
Detection Confidence:80%
Script Type:Latin
HTML Lang Attribute:en
Text Length:768 chars
Detector Agreement:100%

Website Classification

Primary Category

technology software78% confidence
Type: static
Method: ml+structural

All Detected Categories

technology software
78%
documentation technical
55%
adult content
44%

Detected Features

No structural features detected

Domain & IP Information

RequestsIP AddressLocationAS Autonomous System
3172.66.44.184United States
AS13335CLOUDFLARENET
0172.66.47.72United States
AS13335CLOUDFLARENET
02606:4700:310c::ac42:2cb8United States
AS13335CLOUDFLARENET
02606:4700:310c::ac42:2f48United States
AS13335CLOUDFLARENET
34--

Content Similarity HashesFor malware variant detection

TLSH (Trend Micro Locality Sensitive Hash)

Security-focused

Specialized for malware detection and similarity analysis

T13963E721D7BC2C73252E42A4A272373DAC67A517C6021E6975FDB6042B87CAB017F5CE

ssdeep (Context Triggered Piecewise Hashing)

Context-aware

Detects similar content even with modifications

768:AzHZdapzRai6DodyQwB/3X3XMIaSj2fmSR+FkST+ZKuGDY5P3GJZW1k79QvJC:AraplaTDbaSjzT+YuI4P34ZW1k79QvJC

sdhash (Similarity Digest Hashing)

High-precision

High-precision similarity detection for forensic analysis

sdhash:3:70138:VBCInkCCcIIntAEoYuBgBkRWEDNBNhBiHFmAgBIkpDzAqbICAFKgBQ+MyIUGSEhRiQABLMC14CmgALVBCEDCbSgDWaDAwUQI

These hashes enable detection of similar websites and malware variants by comparing content similarity even when exact matches aren't found.

Image Hashes

Perceptual Hashes

Average Hash:f98599ff87878787
Perceptual Hash:becb5632c1c169c9
Difference Hash:112d333b2f2f2f2f
Wavelet Hash:c085b99f87878787
Color Hash:#53ac7e

Other Hashes

Scan History

Scan history not available

Unable to load historical scan data