QuantumChain: Blockchain-Backed Quantum Federated Learning for Financial Fraud Detection

Financial fraud detection is challenged by decentralized data, severe class imbalance, and privacy constraints. This paper presents QuantumChain, a secure Quantum Federated Learning QFL framework that combines hybrid quantum-classical neural networks, encrypted federated aggregation,...

Towards Quantum Machine Learning for Assessing the Resilience of Post-Quantum Cryptography

The potential capabilities of quantum computers motivated the development of cryptographic protocols suitable for securing communication against adversaries with access to large fault-tolerant quantum computers. However, even though current quantum computers are limited in terms of size and...

openssl: OpenSSL TLS 1.3 server may choose unexpected key agreement group

A key group selection preference flaw has been discovered in OpenSSL. An OpenSSL TLS 1.3 server may fail to negotiate the expected preferred key exchange group when its key exchange group configuration includes the default by using the "DEFAULT" keyword. A less preferred key exchange may be used...

Quantum Machine Learning Approaches for Coordinated Stealth Attack Detection in Distributed Generation Systems

Coordinated stealth attacks are a serious cybersecurity threat to distributed generation systems because they modify control and measurement signals while remaining close to normal behavior, making them difficult to detect using standard intrusion detection methods. This study investigates quantu...

Hybrid Quantum-Classical Autoencoders for Unsupervised Network Intrusion Detection

Unsupervised anomaly-based intrusion detection requires models that can generalize to attack patterns not observed during training. This work presents the first large-scale evaluation of hybrid quantum-classical HQC autoencoders for this task. We construct a unified experimental framework that...

A Non-Monotonic Relationship: an Empirical Analysis of Hybrid Quantum Classifiers for Unseen Ransomware Detection

Detecting unseen ransomware is a critical cybersecurity challenge where classical machine learning often fails. While Quantum Machine Learning QML presents a potential alternative, its application is hindered by the dimensionality gap between classical data and quantum hardware. This paper...

Quantum Machine Learning for UAV Swarm Intrusion Detection

Intrusion detection in unmanned-aerial-vehicle UAV swarms is complicated by high mobility, non-stationary traffic, and severe class imbalance. Leveraging a 120 k-flow simulation corpus that covers five attack types, we benchmark three quantum-machine-learning QML approaches - quantum kernels,...