MOBIUSI INC00This corpus contains webpage text, titles, bylines, and publisher claims from pages purporting to issue botnet alerts or incident response advisories, paired with reference webpages from the claimed organizations. Each sample assesses the relationship between a candidate page and its claimed publisher, with labels for authentic publication, repost, impersonation, or inconclusive attribution. It supports training and evaluating models for publisher identity verification and source reliability assessment, as well as cybersecurity advisory review and threat intelligence workflows.
Overview
Preview
Schema
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| confidence | number | The model's confidence in the authenticity assessment, expressed from 0 to 1. |
| candidate_page | object | The submitted advisory page and its associated publisher claims. |
| authenticity_label | string | The assessment of the candidate page's relationship to its claimed publisher. |
| supporting_evidence | array | Relevant observations from the candidate page or reference publisher pages that support the assessment. |
| assessment_rationale | string | A concise explanation of the evidence supporting the authenticity label. |
| reference_publisher_pages | array | Reference webpages associated with the organization claimed as the candidate page publisher. |
Trust
Authorization Type
Proprietary - Commercial AI Training License (No Redistribution)
Commercial Use
Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization
No PII, no real company names, simulated scenarios follow industry standards
Compliance System
Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs
FAQ
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@dataset{Mobiusi2026,
title={Botnet Security Advisory Publisher Identity and Impersonation Verification Corpus},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/3278863fd5aae2dd3782c0abd1f64992?dataset_scene_id=10},
urldate={2026-11-02},
keywords={botnet advisories,cybersecurity corpus,source verification dataset,publisher identity verification,security advisory impersonation detection,cyber threat intelligence,web text classification,information reliability assessment},
version={1.0}
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