A. Mir

Open access Jul 2026

Byzantine-repeater-aware mediated multi-party QKD in untrusted networks: a corrected-frame finite-key security framework via entropy accumulation

Mediated multi-party quantum key distribution (M-MQKD) is studied for repeater-assisted quantum networks with an untrusted repeater layer and end users restricted to single-qubit local operations and one-way quantum reception. Building on the 1D+star mediated construction, the repeater path is treated as an untrusted measurement layer and the security analysis adopts a corrected-frame GHZ model. Under authenticated timing windows, trusted user-side measurements, a calibrated Check observable, and an explicit public-reconciliation transcript, we establish a corrected-frame finite-key theorem yielding a conference-key length bound from two experimentally accessible statistics: Share-mode disagreement and Check-mode parity-violation. The virtual phase-error variable for privacy amplification is related to the corrected-frame Check parity observable by a stabilizer-complementarity argument with quantum side information, while the entropy term in entropy accumulation is replaced by a predeclared affine lower bound on 1−h2(x)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$1- h_{2} (x)$\end{document}. Error-correction leakage is accounted for through a designated-leader pairwise reconciliation transcript, including syndrome traffic, verification tags, and auxiliary public messages. The protocol is hardened by announcement-consistency traps, salted commit-then-open commitments over modes and parameter-estimation outcomes, randomized reveal order, and authenticated timing windows. These mechanisms detect or exclude dishonest classical disclosures before parameter estimation and treat trap failures as an abort/detection statistic rather than an unproved entropy penalty. The framework gives an auditable finite-key extraction rule as a function of observed error rates, finite-sampling radii, reconciliation leakage, EAT finite-size penalties, trap thresholds, and commitment length. Numerical evaluation maps rate-security trade-offs and gives parameter-selection guidance, supporting resilient infrastructure for secure multi-party quantum communication.

Hamza Sohail, B. Khan, A. Mir et al. · EPJ Quantum Technology · 0 citations

Open access Aug 2026

Graph‐Based Generative Adversarial Network for Adaptive IoT Intrusion Detection

The rapid expansion of the internet of things (IoT) has enabled large‐scale connectivity across healthcare, smart homes, industrial automation, and intelligent infrastructure. However, this growth has also increased the exposure of IoT environments to complex and evolving cyber threats. Traditional intrusion detection systems, particularly signature‐based approaches, are often ineffective against previously unseen attacks and struggle to adapt to the heterogeneous and dynamic nature of IoT traffic. To address these challenges, this study proposes a hybrid intrusion detection framework that combines generative adversarial learning with graph attention‐based modeling. The proposed model leverages adversarial data generation to improve the representation of minority attack classes and employs graph attention mechanisms to capture structural dependencies among communicating entities. The framework was evaluated using the UNSW‐NB15 dataset and compared with baseline deep learning models, including generative adversarial networks, graph convolutional networks, and graph attention networks. The proposed method achieved an accuracy of 81.23%, precision of 83.89%, recall of 78.01%, and F1‐score of 80.84% on the held‐out test set, while also reducing false‐positive and false‐negative rates relative to the comparison models. The results demonstrate the effectiveness of combining adversarial data augmentation with graph attention‐based representation learning under the controlled, offline evaluation conditions used in this study. Although the framework may be relevant to IoT and industrial cybersecurity applications, its scalability, real‐time performance, edge‐device feasibility, and effectiveness in operational environments require further experimental validation.

M. H. Alanazi, A. Mir, Asma A. Alhashmi et al. · Engineering Reports · 0 citations