Security Scan Report: mobilewallets.io

Redirected to:
https://mobilewallets.io/lander
Submitted: Oct 4, 2026, 11:12:51 PMCompleted: Oct 4, 2026, 11:14:23 PMpubliccompleted

This website contacted 11 IPs in 5 countries across 5 domains to perform 25 HTTP transactions. The main domain is mobilewallets.io and was registered 19 years ago.

Submitted URL: https://mobilewallets.io

Effective URL:

https://mobilewallets.io/lander
Redirected

AI Security Verdict

Low Risk

Confidence: 78%

2
Risk Score

GoDaddy parked-domain lander with no forms, no impersonation of another brand, and no threat-intel, YARA, or IDS hits. Unranked status is a weak prior only; nothing malicious found.

Risk Factors (2)
Domain is unranked in Cisco Umbrella top 1M (weak prior only)
Parked domain with auto-generated 'Related Search Topics' links may redirect users to ad or affiliate content of unknown quality
Safety Factors (4)
Well-established domain (882 days, registered 2024)
No forms of any kind and no credential or payment collection
No threat-intelligence, YARA, IDS, or kit-roster matches
No cross-origin credential exfiltration
Domain age information unavailable

Details

Page Title

N/A

Scan Type

public

Domain Name Analysis

The domain name 'mobilewallets.io' uses the British Indian Ocean Territory country-code top-level domain (.io) while skipping any subdomain. Its registrable label 'mobilewallets' stretches across 13 characters split between five vowels and 8 consonants. It segments into two words: mobile, wallets. Expect 6.5 characters per word on average. No strong language cues emerged from the frequency lists.

Screenshot

Security scan screenshot of https://mobilewallets.io

Page Load Overview

11.95s
Total Load Time
330 KB
Total Size

Language Analysis

Primary Language

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

Detection Details

HTML Lang Attribute:en
Text Length:524 chars
Detector Agreement:100%

Website Classification

Primary Category

unknown0% confidence
Type: dynamic
Method: structural

All Detected Categories

No categories detected

Detected Features

No structural features detected

Domain & IP Information

RequestsIP AddressLocationAS Autonomous System
515.197.148.33Aws · CLOUDUnited States
AS16509Amazon.com, Inc.
22.19.176.185Akamai · CDNDublin, Leinster, Ireland
AS20940Akamai International B.V.
243.205.79.201Aws · CLOUDMumbai, Maharashtra, India
AS16509Amazon.com, Inc.
223.67.142.207Akamai · CDNFrankfurt am Main, Hesse, Germany
AS16625Akamai Technologies, Inc.
252.222.236.60Cloudfront · CDNUnited States
AS16509Amazon.com, Inc.
23.33.130.190Aws · CLOUDUnited States
AS16509Amazon.com, Inc.
22.19.176.163Akamai · CDNDublin, Leinster, Ireland
AS20940Akamai International B.V.
223.211.47.71Akamai · CDNWarsaw, Mazovia, Poland
AS16625Akamai Technologies, Inc.
252.222.236.107Cloudfront · CDNUnited States
AS16509Amazon.com, Inc.
213.207.108.220Aws · CLOUDMumbai, Maharashtra, India
AS16509Amazon.com, Inc.
2511--

Detected Technologies1

Content Similarity HashesFor malware variant detection

TLSH (Trend Micro Locality Sensitive Hash)

Security-focused

Specialized for malware detection and similarity analysis

T136B25DFE975011AAE19663EFA410B5ADB633707DBA83CA91D3D89E4062C3C1DCC149DB

ssdeep (Context Triggered Piecewise Hashing)

Context-aware

Detects similar content even with modifications

384:xlARZeJ+gPr6ivfNXIT3R+RDMsQGplsVHKogWGdY9h3o7fYcJmQn6pXE:EI/uVVFi+PefYcT4XE

sdhash (Similarity Digest Hashing)

High-precision

High-precision similarity detection for forensic analysis

sdhash:3:24452:MZVgQAxcRoFgNCEpIRQYAwEA8RBrTGwFRAAVRR2QBVtBOULAJAQHG0WwYYRUgAkEbzkSkBFAzGIKQZAYPYAAgehSQgILPQQC

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:ffd9e7e7e7e7e7e7
Perceptual Hash:f3c9888896b6b696
Difference Hash:a2330c0e0c0c0c0c
Wavelet Hash:7e98c2c2c2c2c3c3
Color Hash:#6ce083

Other Hashes

Crop Resistant:a2330c0e0c0c0c0c

Scan History

Scan history not available

Unable to load historical scan data