Security Scan Report: www.liv-coll.ac.uk

Redirected to:
https://liv-coll.ac.uk/
Site favicon
Submitted: Completed: publiccompleted
This website contacted 25 IPs in 2 countries across 12 domains to perform 85 HTTP transactions. The main domain is liv-coll.ac.uk.

Submitted URL: http://www.liv-coll.ac.uk

Effective URL:
https://liv-coll.ac.uk/
Redirected

AI Security Verdict

Safe Website

Confidence: 88%

1
Risk Score

Legitimate City of Liverpool College website: own-brand content, no credential or payment forms, no threat-intelligence, malware or IDS hits. Only a minor staging-host link was noted.

Safety Factors (4)
Self-branding consistent with the domain; no impersonation of another entity
No password, payment or email-harvesting fields on the page
No Indicators of Compromise, IDS alerts, malware patterns or obfuscated/malicious JavaScript
Domain uses an academic .ac.uk namespace and the page publishes matching institutional contact details and physical campus addresses
Domain age information unavailable

Details

Page Title

Home | The City of Liverpool College

Scan Type

public

Domain Name Analysis

Within the United Kingdom country-code top-level domain (.ac.uk), 'www.liv-coll.ac.uk' is registered with subdomain 'www'. Its registrable label 'liv-coll' stretches across 8 characters holding 2 vowels versus five consonants, along with 1 hyphen. Splitting it apart reveals two words: liv, coll. Median word length comes out to 3.5 characters. No strong language cues emerged from the frequency lists.

Screenshot

®*> The City {_., of Liverpool Study Employers Support College Life Visit About Student Hub “S* College el Study at | THECITYOF — LIVERPOOL COL @courses | | () Careers (Q “cane Recent Searches:...

Page Load Overview

5.76s
Total Load Time
8.4 MB
Total Size

Language Analysis

Primary Language

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

Detection Details

HTML Lang Attribute:en-GB
Text Length:6,439 chars
Detector Agreement:100%

Website Classification

Primary Category

adult content61% confidence
Type: dynamic
Method: ml+structural

All Detected Categories

adult content
61%
education
45%
government public service
26%
corporate
25%

Detected Features

OG: website
Schema.org

Domain & IP Information

RequestsIP AddressLocationAS Autonomous System
13162.159.137.54Cloudflare · WAFUnited States
AS13335Cloudflare, Inc.
3162.159.136.54Cloudflare · WAFUnited States
AS13335Cloudflare, Inc.
3184.24.77.134Akamai · CDNFrankfurt am Main, Hesse, Germany
AS20940Akamai International B.V.
3192.178.183.95Google · CDNUnited States
AS15169Google LLC
318.245.31.55Cloudfront · CDNUnited States
AS16509Amazon.com, Inc.
365.8.131.100Cloudfront · CDNUnited States
AS16509Amazon.com, Inc.
3184.24.77.135Akamai · CDNFrankfurt am Main, Hesse, Germany
AS20940Akamai International B.V.
33.174.46.104Cloudfront · CDNUnited States
AS16509Amazon.com, Inc.
399.84.152.35Cloudfront · CDNUnited States
AS16509Amazon.com, Inc.
313.32.99.48Cloudfront · CDNNew York, New York, United States
AS16509Amazon.com, Inc.
8525--

Content Similarity HashesFor malware variant detection

TLSH (Trend Micro Locality Sensitive Hash)

Security-focused

Specialized for malware detection and similarity analysis

T15054A5F39535023D3267634D314FAA24B3A254FADA220FF47AFE2068DBC92DD6175216

ssdeep (Context Triggered Piecewise Hashing)

Context-aware

Detects similar content even with modifications

3072:tbh7BWU54wz3/UTrKKalgbVybVtbVpCOABZ/e:MeKalgbVybVtbV4ON

sdhash (Similarity Digest Hashing)

High-precision

High-precision similarity detection for forensic analysis

sdhash:3:281135:ShINIM2MINIAAGEBgIQABOyECEByRCgDdgYEAYUkFowArAiAFpTwQDWgDgcRA3IWKkYRCpsgILAkLkBaGcOECQDCJEAHgY65

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:000f1f7f73f17101
Perceptual Hash:a0153f6439e84b3f
Difference Hash:27fefb97e3c7a3bb
Wavelet Hash:000f3f7f73f17101
Color Hash:N/A

Scan History

Scan history not available

Unable to load historical scan data