The dynamic and constantly evolving landscape of cyber threats demands innovative methods capable of adapting to the complex relationships and structures inherent in network data. Traditional methods often struggle to adequately capture the intricacies of dynamic networks, especially in terms of evolving temporal dynamics and multiscale dependencies. The proposed solution, Enhanced V-GCN, combines the structural insights of Graph Convolutional Networks (GCNs) with the temporal modeling capabilities of Variational Autoencoders (VAEs), further augmented by multiscale convolutions and attention mechanisms. Multiscale convolutions enable the model to aggregate information across broader neighborhood ranges, while attention mechanisms prioritize the most critical nodes and edges, dynamically adapting to changes within the network. This enhanced approach allows V-GCN to effectively capture both nodal and structural patterns, significantly improving performance in node classification tasks. The Enhanced V-GCN model has demonstrated superior performance in node classification, outperforming baseline models with an accuracy of 98.00%, precision of 97.93%, recall of 98%, and an F1-score of 97.92%, indicating robust classification capabilities and exceptional generalization across diverse network structures.

Mediated multi-party quantum key distribution (M-MQKD) is studied for repeater-assisted quantum networks with an untrusted repeater layer and end users res

Modern networks generate high-dimensional traffic that requires numerous features for effective intrusion detection and cybercrime defence, creating a critical challenge for quantum approaches, where qubit availability remains the primary bottleneck for near-term implementation. We present a quantum-enhanced framework that employs Dual-Parameter encoding, using both the polar angle \(\theta\) and azimuthal phase \(\phi\) of each qubit to encode two normalized features, combined with multi-metric anomaly scoring and calibrated false-positive control. Our detector forms a composite score \(\begin{array}{c}S\left(x\right)={w}_{1}(1-F)+{w}_{2}{{D}\scriptscriptstyle_{{KL}}}+{w}_{3}\varDelta{S}_{v\scriptscriptstyle N}\end{array}\) that unifies quantum fidelity (global state similarity), Kullback-Leibler divergence (distributional shift), and von Neumann entropy change (uncertainty shift). The decision threshold is set by quantile calibration on benign validation, and via the Dvoretzky-Kiefer-Wolfowitz inequality, comes with an explicit finite-sample upper bound on the false-positive rate under i.i.d. assumptions. For a selected feature set of size \({N}_{sel}\), our Dual-Parameter encoding reduces the data-qubit requirement from \({N}_{sel}\) to \(\left\lceil {N_{{sel}} /2} \right\rceil\) at matched-feature count. This qubit reduction preserves the entangling topology but adds one additional single-qubit encoding rotation \(R_{z}\) per data qubit and a Hadamard readout layer; when a SWAP-test ancilla is used, the total count becomes \(\left\lceil {N_{{sel}} /2} \right\rceil+1\). We evaluate DP-QIDS on UNSW-NB15 and CIC-IDS2017 and reproduce the baseline’s DDoS setting both at a richer selected feature count and under the baseline’s standard 87→4 classical compression pipeline applied to both encoders, to ensure direct matched-setting comparisons at the encoder level. Ablations show that fidelity captures correlation-shifting attacks, while KL/entropy detects randomized marginals; their fusion yields consistently reliable operation. We further provide a complexity analysis, deployment architecture, and streaming variants with drift-aware recalibration. Our Dual-Parameter Quantum Intrusion Detection System (DP-QIDS) advances quantum intrusion detection from proof-of-concept toward auditable, resource-aware deployment, connecting simulator studies with practical security operations for resilient infrastructure.

Transparent and efficient water management is becoming a necessity for smart cities worldwide. Recently, technological advancements in water system infrastructure are helping address modern challenges for sustainable water management, particularly in densely populated cities experiencing rapid urbanization. Smart water metering (SWM) systems are increasingly deployed because they generate detailed consumption data and enable early identification of system-wide water losses. However, privacy and security risks, including unauthorized ownership, data manipulation, and tampering, can create mistrust between utilities and citizens, especially when combined with issues such as system-wide water loss, unhealthy physical water infrastructure, and billing inaccuracies. This study introduces a conceptual framework that explores how non-fungible tokens (NFTs) might contribute to more transparent and trustworthy SWM operations. In this approach, each meter is assigned a unique NFT-based digital identity, allowing installation records, maintenance logs, ownership status, and usage history to be represented in a structured and traceable way. The framework is compared with existing identity and asset-representation mechanisms, including PKI, DIDs, and non-tokenized blockchain registries. A simple data flow model illustrates how NFTs could function as digital identities for physical meters, and the role of digital water governance in authorizing token issuance is discussed. These ideas are conceptual rather than operational; they outline how meter-related metadata could become more auditable and interoperable across stakeholders. This paper also proposes a phased pilot pathway as a future validation plan, together with an illustrative evaluation framework to guide pilot design, along with potential security challenges and conceptual mitigation strategies. Moreover, the framework highlights how NFT-enabled identities and smart contracts may support improvements in transparency, accountability, and user trust in SWM systems. It is intended as a foundation for future empirical evaluation in smart-city contexts, not as evidence of a deployed or validated solution.