Research Article
Advanced Quantum-Resilient Frameworks for Anomaly Detection in Multi-Tenant Hybrid Cloud Environments
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 187 - Issue 39 |
| Published: September 2025 |
| Authors: Bareq M. Khudhair, Karrar M. Khudhair |
10.5120/ijca2025925680
|
Bareq M. Khudhair, Karrar M. Khudhair . Advanced Quantum-Resilient Frameworks for Anomaly Detection in Multi-Tenant Hybrid Cloud Environments. International Journal of Computer Applications. 187, 39 (September 2025), 30-38. DOI=10.5120/ijca2025925680
@article{ 10.5120/ijca2025925680,
author = { Bareq M. Khudhair,Karrar M. Khudhair },
title = { Advanced Quantum-Resilient Frameworks for Anomaly Detection in Multi-Tenant Hybrid Cloud Environments },
journal = { International Journal of Computer Applications },
year = { 2025 },
volume = { 187 },
number = { 39 },
pages = { 30-38 },
doi = { 10.5120/ijca2025925680 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2025
%A Bareq M. Khudhair
%A Karrar M. Khudhair
%T Advanced Quantum-Resilient Frameworks for Anomaly Detection in Multi-Tenant Hybrid Cloud Environments%T
%J International Journal of Computer Applications
%V 187
%N 39
%P 30-38
%R 10.5120/ijca2025925680
%I Foundation of Computer Science (FCS), NY, USA
Abstract
This research addresses the compounded security risks in multi- tenant hybrid cloud environments arising from advanced cyber threats and the emerging capabilities of quantum computing. The study proposes Q-ZAP, a Quantum-Resilient Zero-Trust Anomaly- detection Platform that integrates Post-Quantum Cryptography (PQC) and a Hybrid Quantum-Classical Machine Learning (QML) model within a Zero-Trust Architecture (ZTA). The core component is a Hybrid Autoencoder (HAE) designed for unsupervised anomaly detection in high-dimensional cloud log data. The system employs NIST-standardized PQC algorithms (ML-KEM and ML-DSA) to secure both control and data planes. Experimental results in a simulated environment demonstrate a 13.3% improvement in F1-score over classical baselines, with acceptable overhead from PQC integration.
References
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