The paper proposes Q-AGNN, a Quantum-Enhanced Attentive Graph Neural Network, to improve intrusion detection by modeling network flows as graphs and leveraging quantum circuits to capture complex rela…

The paper proposes a novel Meta-Quantum Ensemble (MQE) framework, which fuses outputs from Quantum Support Vector Machines (QSVMs) and Quantum Neural Networks (QNNs) using a Random Forest meta-learner…

This survey provides a detailed overview of quantum adversarial machine learning, examining existing attacks, novel quantum-enhanced defense strategies, and the theoretical challenges in securing quan…

This paper provides a comprehensive, system-level taxonomy for designing quantum-resistant network architectures, moving beyond simple protocol substitutions to address key distribution and management…

This survey provides a comprehensive review of the security challenges, threats, and mitigation strategies associated with the rapid advancement of quantum computing.

The paper presents Broken Quantum, a comprehensive formal security audit that identifies 547 security vulnerabilities across 45 open-source quantum computing simulators, revealing critical flaws in me…

The paper evaluates quantum machine learning for detecting anomalies in UAVs using a rigorous, leakage-free methodology, showing that a hybrid XGBoost + Data Reuploading classifier performs well, part…

This paper surveys the security vulnerabilities of Variational Quantum Circuits (VQCs) to backdoor attacks, detailing various attack mechanisms and analyzing current detection and defense strategies.

The paper proposes QShield, a hybrid quantum-classical neural network architecture, which significantly enhances the adversarial robustness of deep learning models against various attacks.

This paper quantifies the latency impact of increasing certificate chain sizes required by Post-Quantum Cryptography (PQC) on TLS Time to First Byte (TTFB), finding that Merkle Tree Certificates (MTC)…

The paper proposes a novel three-layer metric framework to comprehensively evaluate quantum circuit integrity by combining structural, operational, and interaction-level analyses, demonstrating that n…

The paper reviews adversarial machine learning vulnerabilities and proposes conceptual frameworks for enhancing AI robustness by integrating quantum computing techniques.

The paper reviews the vulnerability of AI to adversarial attacks and proposes conceptual frameworks for enhancing AI robustness by integrating quantum computing techniques.

The paper proposes a layered, modular network architecture combining Quantum Key Distribution (QKD) and Post-Quantum Cryptography (PQC) to achieve scalable, end-to-end post-quantum security in multi-h…

QML-PipeGuard introduces a contract-based framework that monitors the behavioral fingerprint of quantum machine learning pipelines to detect both hardware drift and malicious channel substitution.

The paper proposes a provably secure path validation protocol for large-scale Quantum Key Distribution (QKD) networks that allows receivers to verify network compliance without revealing sensitive top…

The paper introduces a multi-surface evidence framework to provide comprehensive observability for post-quantum TLS migration, enabling robust measurement of session behavior and endpoint capabilities…

This paper demonstrates a non-disruptive, sidecar-based integration of NIST-standardized Post-Quantum Cryptography (PQC) into an open-source 5G core, showing that while it introduces a predictable lat…

The paper proposes a Quantum Augmented Microgrid (QuAM) framework that integrates quantum networking concepts to enhance the cybersecurity, confidentiality, and privacy of decentralized microgrids aga…

The paper argues that current lattice-based post-quantum cryptography, which relies on injecting noise, is not unconditionally secure because advanced quantum error correction and learning techniques…