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Attributed metadata history

REPORTPUBLIC METADATA

AI model spots spoofed website addresses with up to 99% accuracy

A machine-learning system can identify fake, or spoofed, website addresses, according to research published in the International Journal of Electronic Security and Digital Forensics. The work offers a new tool to protect users against phishing attacks, in which attackers direct someone to a deceptive website to steal information such as bank logins or personal and private data.

Reference details

Source category
Research & news
Report title
AI model spots spoofed website addresses with up to 99% accuracy
First reported
Sep 16, 2026 19:20 UTC
Last reported
Sep 16, 2026 19:20 UTC
Catalog updated
Oct 08, 2026 03:51 UTC
Record ID
5bcdc0f7e7672234ad01b81e49432f7911cfb159062fcc9fa6215b090ed8be20

Published indicators

Network indicators are visibly defanged. This page does not make them clickable or send them to another service.

No validated file hashes or network indicators are stored for this record.

Source observations

src_1f70e50a5ea23d3177

Source-provided label
Not reported
Evidence class
mentioned in report
Confidence
Not reported
Observed
Sep 16, 2026 19:20 UTC
Retrieved
Oct 08, 2026 03:51 UTC

This record contains metadata and attribution only. It does not host, download, unpack, render, or execute malware samples.

Feed collection status

Sources represented on this page. Publication and observation dates describe the content; successful batch dates describe local collection. A recent batch does not certify complete coverage or current provider availability.

1 sources: 0 stale, 0 needing attention, 0 paused, 1 unverified.

Review source update dates and status
  • src_1f70e50a5ea23d3177Collection status unverifiedLast successful batch: Unverified

Stale means older than twice the recorded collection interval, with a minimum of one hour; without an interval, the threshold is 24 hours. Cached content remains available while a feed is stale, paused, or unverified.