Volume 22, Issue 3 (September 2026)                   IJEEE 2026, 22(3): 4112-4112 | Back to browse issues page

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Kosari A. An Integrated Threat Model: Quantum Machine Learning Attacks on Satellite Communications and a Multi-Layered Defense Framework. IJEEE 2026; 22 (3) :4112-4112
URL: http://ijeee.iust.ac.ir/article-1-4112-en.html
An Integrated Threat Model: Quantum Machine Learning Attacks on Satellite Communications and a Multi-Layered Defense Framework
Arash Kosari
Abstract:   (173 Views)
Satellite communications are the invisible backbone of our connected world, supporting everything from daily internet access to critical military missions. Yet, beneath their importance lies a hidden vulnerability: the physical layer remains exposed to increasingly sophisticated cyber threats. In this paper, we explore how quantum technologies could be weaponized against these systems and how they might be defended. We present an integrated attack model that brings together Quantum Support Vector Machines (QSVM) for highly precise signal prediction and Quantum Random Number Generators (QRNG) for stealthy noise injection. Using realistic simulations on Qiskit, GNU Radio, and MATLAB, we show that such an attack can succeed 85% of the time, with only a 15% chance of being detected, while causing a 30% rise in bit errors. These results underline the disruptive potential of quantum-enhanced adversaries. To counter this, we propose a layered defense strategy combining post-quantum cryptography, machine learning–driven intrusion detection, adaptive signal processing, and hardware safeguards. Our findings not only reveal the scale of the challenge but also offer a roadmap toward securing future satellite networks in the quantum era.
Keywords: Quantum Machine Learning, Quantum Support Vector Machine (QSVM), Quantum Random Number Generator (QRNG), Satellite Communications Security, Intrusion Detection Systems.
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Type of Study: Research Paper | Subject: Satellite Communications
Received: 2025/09/07 | Revised: 2026/05/10 | Accepted: 2026/01/27
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.