Security Scan Report: 1xbet.et

Redirected to: https://1xbet.et/en/block

Submitted: Jan 2, 2026, 1:05:15 PMCompleted: Jan 2, 2026, 1:06:55 PMpubliccompleted
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Summary

This website contacted 13 IPs in 2 countries across 10 domains to perform 60 HTTP transactions. The main domain is 1xbet.et.

Submitted URL: https://1xbet.et/en/line/tennis/2492483-united-cup-women/292787619-maria-sakkari-naomi-osaka

Effective URL: https://1xbet.et/en/blockRedirected

AI Security Verdict

Confirmed Scam

Confidence: 95%

10
Risk Score

New, unranked domain impersonates 1xBet brand – confirmed scam.

Risk Factors
Brand impersonation on an unranked, newly registered domain
Domain age < 90 days while mimicking a known brand
Redirect to a block page without providing expected betting content
Domain age information unavailable

Details

Page Title

1xBet

Scan Type

public

Language

🏳️

UNKNOWN

(0% confidence)

Category

unknown

(0%)

Domain Information

You're looking at domain '1xbet.et' on the Ethiopian country-code top-level domain (.et) with no subdomain. The registrable portion '1xbet' spans 5 characters split between 1 vowel and 3 consonants, notching one digit. Tokenizing the label suggests three words: 1, x, bet. Average segment length settles at 1 character. No strong language cues emerged from the frequency lists.

Screenshot

Security scan screenshot of https://1xbet.et/en/line/tennis/2492483-united-cup-women/292787619-maria-sakkari-naomi-osaka

Page Load Overview

24.17s
Total Load Time
6
HTTP Requests
2
Domains
3 KB
Total Size

Language Analysis

Primary Language

🏳️UNKNOWN
Code: unknown
Confidence:0%

Detection Details

Language Code:unknown
Detection Confidence:0%
0
Detector Agreement:0%

Website Classification

Primary Category

unknown0% confidence
Type: static
Method: structural

All Detected Categories

No categories detected

Detected Features

No structural features detected

Domain & IP Information

RequestsIP AddressLocationAS Autonomous System
6142.250.186.168Germany
0142.251.208.3Germany
087.250.250.119GermanyUnknown
077.88.21.119GermanyUnknown
0216.239.32.36GermanyUnknown
0216.239.34.36GermanyUnknown
092.223.124.62Frankfurt am Main, Hesse, Germany
AS199524G-Core Labs S.A.
0108.177.15.157GermanyUnknown
045.135.121.30Amsterdam, North Holland, Netherlands
AS56630Melbikomas UAB
045.54.49.5GermanyUnknown
613--

Detected Technologies1

40%

Content Similarity HashesFor malware variant detection

TLSH (Trend Micro Locality Sensitive Hash)

Security-focused

Specialized for malware detection and similarity analysis

T153B20B46F4ACB01ABBF782C8883966C7F9AFE31FC249919591FD85D40E83A57B703901

ssdeep (Context Triggered Piecewise Hashing)

Context-aware

Detects similar content even with modifications

384:4cihZx4dRnRhquokVgQVCr3rywsf3EqOt8H7eOHdb/0+Tc:VRh8kVsr3rywy/OaSO9b/0+Q

sdhash (Similarity Digest Hashing)

High-precision

High-precision similarity detection for forensic analysis

sdhash:3:23606:YGQJfSAFMWhVYoDWUilqQKwCAOQgiijCASkC0MSKBbIgEYMIQBEFiZCJQAS0BVgCIlmBJOIh5eBIVFtMKGPqOIYArCFWVIBc

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:0000181818000000
Perceptual Hash:c9c9626299c97673
Difference Hash:80ccb2b2b2f0c8f0
Wavelet Hash:f8f8f8f8f8703018
Color Hash:#3a7841

Other Hashes

Scan History

Scan history not available

Unable to load historical scan data