Security Scan Report: golvpoolen.se

Redirected to:
https://www.golvpoolen.se/
Site favicon
Submitted: Completed: publiccompleted
This website contacted 35 IPs in 3 countries across 29 domains to perform 106 HTTP transactions. The main domain is golvpoolen.se.

Submitted URL: https://golvpoolen.se

Effective URL:
https://www.golvpoolen.se/
Redirected

AI Security Verdict

Safe Website

Confidence: 88%

1
Risk Score

Established ~10-year-old Swedish flooring, tile and bathroom retailer; self-branded domain matching its own name, no forms, no Indicators of Compromise, YARA or phishing/malware IDS hits. Clean.

Safety Factors (6)
Established domain with a ~10-year registration history
Self-branded site whose name matches its own domain
No credential, login or payment forms present
Physical store addresses listed in Helsingborg, Göteborg, Malmö and Stockholm with phone/service contacts
No Indicators of Compromise, no YARA hits, no malicious kits, no Safe Browsing or IDS phishing/malware alerts
Content is a normal Swedish flooring/tile/bathroom e-commerce catalogue
Domain age information unavailable

Details

Page Title

Golvpoolen - bäst på golv, kakel, och badrum! - Golvpoolen.se

Scan Type

public

Domain Name Analysis

You're looking at domain 'golvpoolen.se' on the Swedish country-code top-level domain (.se) with no subdomain. The registrable portion 'golvpoolen' spans 10 characters with four vowels and 6 consonants. Tokenizing the label suggests 4 words: go, lv, pool, en. Median word length is two characters. No strong language cues emerged from the frequency lists.

Screenshot

Security scan screenshot of https://golvpoolen.se

Page Load Overview

14.04s
Total Load Time
2.6 MB
Total Size

Language Analysis

Primary Language

🇸🇪Swedish
Code: sv
Confidence:56%
Script:Latin
Direction:ltr

Detection Details

HTML Lang Attribute:en
Text Length:1,378 chars
Detector Agreement:100%
Language mismatch: Declared as en but detected as sv

Website Classification

Primary Category

social media network81% confidence
Type: spa
Method: ml+structural

All Detected Categories

social media network
81%
e-commerce shopping
76%
blog personal website
42%
corporate
25%

Detected Features

OG: website
Schema.org

Domain & IP Information

RequestsIP AddressLocationAS Autonomous System
434.128.157.171Google · CDNKansas City, Missouri, United States
AS396982Google LLC
395.101.54.99Akamai · CDNFrankfurt am Main, Hesse, Germany
AS20940Akamai International B.V.
3216.239.38.21Google · CDNUnited States
AS15169Google LLC
3104.20.44.120Cloudflare · WAFUnited States
AS13335Cloudflare, Inc.
3146.75.122.208Fastly · CDNFrankfurt am Main, Hesse, Germany
AS54113Fastly, Inc.
3184.24.77.169Akamai · CDNFrankfurt am Main, Hesse, Germany
AS20940Akamai International B.V.
3172.66.163.83Cloudflare · WAFUnited States
AS13335Cloudflare, Inc.
3104.18.1.22Cloudflare · WAFUnited States
AS13335Cloudflare, Inc.
3142.251.14.97Google · CDNUnited States
AS15169Google LLC
318.156.47.223Aws · CLOUDFrankfurt am Main, Hesse, Germany
AS16509Amazon.com, Inc.
10635--

Content Similarity HashesFor malware variant detection

TLSH (Trend Micro Locality Sensitive Hash)

Security-focused

Specialized for malware detection and similarity analysis

T176642A1AE834D819E4D34E5DB93E6EBEA9CDC056E291C879F6ECCA3942C283D17D3150

ssdeep (Context Triggered Piecewise Hashing)

Context-aware

Detects similar content even with modifications

6144:iTMaIHpkef/WfVE8goKRogPvP+gPDaa1KFX/y9m/L:EIWeWfVE8lKRogPvP+gPDaZFvZ

sdhash (Similarity Digest Hashing)

High-precision

High-precision similarity detection for forensic analysis

sdhash:3:309387:CSIMwYCiEHuFjqOgiYSFbIOGgKyAsCMkTAg4EAICnmgEQFuFJXLJBogDAmoAAApNCJEk4xCKDVJCEMABVCH0Cg4BQQIrgrkC

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:ff99819999bfffff
Perceptual Hash:9d2de6d29cc4262b
Difference Hash:6433333333335d71
Wavelet Hash:bf8901191981bdbf
Color Hash:#87c58a

Scan History

Scan history not available

Unable to load historical scan data