نهج مقترح لتطبيق سياسات الوصول للبيانات وفق انعدام الثقة ودمجها بأمن الوصول السحابي لمواجهة التصيد الاحتيالي الخفي

This study proposes a security framework integrating Zero Trust data access policies with a Cloud Access Security Broker (CASB) to mitigate shadow phishing. It combines continuous identity and device verification, contextual risk assessment, traffic inspection, and data loss prevention. The proposed methodology covers application discovery, behavioral baselining, policy formulation, CASB integration, and controlled attack simulations. Candidate machine learning methods support anomaly detection, traffic classification, and content analysis. Planned evaluation measures include blocking rates, false positives, latency, and session revocation time. Expected benefits are tighter control of unauthorized transfers and reduced lateral movement. The contribution is architectural; effectiveness and performance remain subject to implementation and comparative testing.

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Publication Details

Journal
Humanitarian and Natural Sciences Journal
Published
2026-10-31
DOI
https://doi.org/10.53796/hnsj711/21
Primary Topic
Spam and Phishing Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

نهج مقترح لتطبيق سياسات الوصول للبيانات وفق انعدام الثقة ودمجها بأمن الوصول السحابي لمواجهة التصيد الاحتيالي الخفي

Hadia Awad Mohamed Ahmed
Humanitarian and Natural Sciences Journal
Spam and Phishing Detection
article

نهج مقترح لتطبيق سياسات الوصول للبيانات وفق انعدام الثقة ودمجها بأمن الوصول السحابي لمواجهة التصيد الاحتيالي الخفي

Hadia Awad Mohamed Ahmed
article en

Abstract

This study proposes a security framework integrating Zero Trust data access policies with a Cloud Access Security Broker (CASB) to mitigate shadow phishing. It combines continuous identity and device verification, contextual risk assessment, traffic inspection, and data loss prevention. The proposed methodology covers application discovery, behavioral baselining, policy formulation, CASB integration, and controlled attack simulations. Candidate machine learning methods support anomaly detection, traffic classification, and content analysis. Planned evaluation measures include blocking rates, false positives, latency, and session revocation time. Expected benefits are tighter control of unauthorized transfers and reduced lateral movement. The contribution is architectural; effectiveness and performance remain subject to implementation and comparative testing.

Humanitarian and Natural Sciences JournalVol. 7(11)
University of Khartoum (SD)
Openalex Percentile: Top 6%
Spam and Phishing Detection
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