Unsupervised ML Based Anomaly Detection for Securing BB84 QKD Against Intercept-Resend Attacks and Channel Noise: OC-SVM, iForest, and Ex-iForest

Beyond the QBER Threshold: A Temporal QBER Based Machine Learning Framework for Multi Attack Detection in BB84 QKD

Conventional BB84 Quantum Key Distribution (QKD) systems rely on a fixed 11% Quantum Bit Error Rate (QBER) threshold to detect eavesdropping. However, stealthy attacks can remain below this threshold while still compromising channel security. This paper proposes a temporal QBER based machine learning framework for detecting and classifying eavesdropping attacks in BB84 QKD systems. Rather than relying on average session level QBER, the framework extracts 63 physics-informed temporal features capturing burst behavior, temporal instability, basis dependent asymmetry, and QBER loss interactions. Random Forest, XGBoost, and Support Vector Machine with a Radial Basis Function kernel (SVM-RBF) classifiers are evaluated on seven eavesdropping attacks and a normal channel scenario under noisy and lossy conditions. Averaged over ten independent runs, XGBoost achieves the best performance with 88.01% (0.47%) accuracy and a macro F1 score of 0.8803, while SVM-RBF performs comparably, confirming the robustness of the proposed features. Evaluated as a binary attack-versus-normal detector for comparison with conventional monitoring, a fixed 11% QBER threshold achieves only 25.82% accuracy with a False Negative Rate (FNR) of 0.8477, whereas the proposed framework reduces the FNR to 0.0198, substantially improving detection of stealthy attacks that evade threshold-based monitoring. SHapley Additive exPlanations based (SHAP) explainability shows that physics-informed temporal and channel derived features are highly discriminative for identifying eavesdropping strategies. These results demonstrate that temporal QBER driven machine learning provides an accurate, explainable, and practical framework for multi attack security monitoring in BB84 QKD systems.

Quantum Key Distribution-Enabled Secure Federated Learning with QBER-Based Attack Detection

Federated Learning (FL) enables collaborative model learning without the need to share raw data, but its communication links are vulnerable to interception, replay, and man-in-the-middle (MITM) attacks. The existing key exchange methods rely on computational hardness assumptions, which can be broken by post-quantum attackers. In this paper, a secure federated learning framework improved by Quantum Key Distribution (QKD) is proposed, which integrates BB84-like quantum key generation and authenticated encryption on a per-round basis, as well as Quantum Bit Error Rate (QBER)-assisted intrusion detection. A new cryptographic key is produced in each federated round, making it immune to replay attacks and allowing for detection of tampering. Theoretical calculations show that intercept-resend attacks lead to a minimum expected QBER of 25%, making it easier to detect statistically. Experimental results on the MNIST dataset show near-perfect detection rates for MITM and replay attacks, with QBER values increasing from about 1% (serving as a benign scenario) to about 26% in an attack scenario. Communication overhead is kept below 10%, with negligible computational latency compared to local training. The experiments show that the use of QKD-based key refresh improves FL communication security while still ensuring model convergence.

Dynamically quantum attack detection for quantum key distribution based on deep representations

Cryptographic DoS Amplification in Hybrid Post-Quantum Deployments: Adversarial Algorithm Substitution and Its Countermeasures

Hybrid post-quantum TLS deployments pair classical and post-quantum signature algorithms to resist future quantum adversaries. However, to the best of our knowledge, no active IETF TLS, PQUIP, or LAMPS working-group draft treats CPU-cost adversarial algorithm substitution as a first-class availability threat. We define the Cryptographic Amplification Factor (CAF), a metric for the per-resource cost asymmetry that an adversary induces by forcing a TLS 1.3 server onto a high-cost signature algorithm (SLH-DSA) instead of a low-cost one (ECDSA or ML-DSA). We characterise four adversary classes—insider misconfiguration, supply-chain compromise, remote TLS peer, and protocol man-in-the-middle—demonstrate working proof-of-concept exploits on representative testbeds, and evaluate three defensive primitives under realistic deployment conditions. On the liboqs 0.14.0 reference implementation, CAFrcpu reaches 12,255× on Apple M-series ARM64 and 14,272× on AWS Graviton3 (3904–14,272× across five microarchitectures). Under a representative two-worker hardware security module (HSM) calibration (λ=13.5 TPS), substitution drives the modelled queue utilisation ρ past the stability boundary (1.44×10−4→1.787), a model-independent saturation; on the testbed, we observe server-CPU saturation, accept-queue exhaustion, and dropped connections. Two enforcement defences and a Parallel Signing Architecture provably bound ρ≤α<1 by admission control, regardless of attack rate. The exposed surface is concentrated today in high-assurance and financial-sector deployments and widens as FIPS 206 finalises.

Detecting Practical Attacks for Continuous-Variable Quantum Key Distribution Using Quantum k-Nearest Neighbor

Continuous-variablequantum key distribution (CVQKD) enables information-theoretically secure communications between two legitimate users. However, practical CVQKD systems remain vulnerable to various attacks, while existing machine-learning-based detection schemes suffer from increasing computational complexity when processing large-scale monitoring data. In this paper, we propose a quantum k-nearest neighbor (QkNN)-based multiclass attack detection framework for CVQKD systems. Specifically, physical features extracted from Bob’s monitoring data are encoded into quantum states and subsequently classified using the QkNN algorithm to identify different attack behaviors. Furthermore, by incorporating the attack ratio, retained-data ratio, and attack detection performance into secret key rate analysis, an attack-aware secret key rate model is established to characterize the influence of practical attacks on key generation. Simulation results demonstrate that the proposed scheme achieves high attack classification accuracy while significantly reducing computational complexity. Moreover, the proposed framework establishes a direct connection between attack detection and secret key generation, enabling a more realistic security evaluation for practical CVQKD systems.

AI-Driven Threat Detection and Quantum Cryptography Integration for Cybersecure Communication Systems

Their combination of adversarial AI attacks with cryptographically relevant quantum computers endangers the traditional public-key infrastructure. This paper proposes a hybrid system that combines real-time deep learning-based threat detection and Quantum Key Distribution (QKD) to secure communication systems. A convolutional neural network with attention mechanism detects network anomalies with 98.4% accuracy on the CIC-IDS-2017 dataset. At the same time, a decoy-state BB84 QKD protocol is used to create symmetric keys on a modeled 40 km fiber channel with a quantum bit error rate of less than 2.5%. A new query fragment caching algorithm cuts the key delivery latency by a factor of 37. Empirical evidence demonstrates the system throughput of 850 Mbps and key generation rate of 12.4 kbps providing a viable roadmap to information-theoretically secure communication when using active cyber threats.