The increasing interconnection of smart-grid operational technology, industrial-edge services, and utility information systems creates a critical need for resilient and continuously monitored industrial cyber-physical communication. Although quantum key distribution (QKD) can strengthen session-key establishment for advanced metering infrastructure, distributed energy resources, substation automation, supervisory control, and utility-core services, practical QKD deployments remain vulnerable to implementation-level side-channel attacks that can compromise the cryptographic protection layer without directly targeting conventional network packets. This paper presents a QKD-secured industrial smart-grid cyber-physical system framework for simulating and detecting adaptive side-channel attacks. The proposed 36-node industrial communication architecture integrates AMI devices, DER controllers, PMU and substation automation components, industrial-edge gateways, QKD modules, key-management services, SCADA and utility-core servers, security-operation-center components, and adversarial access points. A 100,000-record cyber-quantum dataset is generated across 12 operating conditions comprising normal communication and 11 adaptive QKD side-channel attacks: detector blinding, time shift, wavelength switching, Trojan-horse probing, photon-number splitting, decoy-state spoofing, RNG bias, calibration manipulation, local-oscillator manipulation, synchronization spoofing, and combined adaptive quantum hacking. Each scenario introduces coupled primary and secondary perturbations across optical, detector, timing, synchronization, randomness, calibration, photon-statistical, leakage, key-generation, encryption, and industrial-network-performance features. To support intelligent industrial security monitoring, the proposed Quantum-aware Mamba–Kolmogorov–Arnold Network (Q-MambaKAN) organizes device, network, QKD, side-channel, encryption, and risk evidence into an ordered cyber-quantum representation processed through selective state-space learning, side-channel attention, nonlinear KAN mapping, adaptive fusion, and multi-task prediction heads. Results show that the QBER increases from 0.071 during normal operation to 0.426 under combined adaptive quantum hacking, while encryption success decreases from 98.1% to 0%. Q-MambaKAN achieves a 99.48% binary detection accuracy, a 99.70% binary F1-score, a 97.60% multiclass macro-F1, and a risk RMSE of 0.021.

This paper proposes a novel method of message cryptography aiming to provide a simple, secure, and highly efficient approach to encryption and decryption. Unlike existing methods that rely on complex logical operations, our method utilizes simple rearrangement operations, reducing computational complexity while ensuring robust security. It employs a sophisticated, high-entropy private key designed to withstand hacking attempts. This key generates two chaotic keys using chaotic logistic map models, which are sorted to form two index keys essential for rearranging message blocks and characters during encryption and decryption. The process is facilitated by two simple operations, Get_index and Get_min, based on the index keys. These operations achieve streamlined procedures without compromising security. The method's speed is evaluated across various message lengths, demonstrating significant improvements in encryption time and throughput. The comparative analysis highlights the superior efficiency of this method compared to existing methods. Rigorous testing confirms that the proposed method meets stringent quality and sensitivity requirements, ensuring robust cryptographic standards. This innovative approach offers a promising solution for secure message encryption and decryption, combining simplicity, security and efficiency to meet the evolving needs of secure communication systems.

Smart water infrastructure increasingly relies on distributed Industrial Internet of Things (IIoT) and SCADA architectures for real-time monitoring, supervisory control, and operational decision-making. However, this integration also creates cyber-physical vulnerabilities in which attackers can maintain plausible communication and supervisory feedback while the underlying process gradually deviates from its true state. This paper presents a distributed smart water IoT-SCADA simulation testbed for investigating Cross-Zone Process–Perception Dissociation Attacks (CZPPDAs), where apparently normal SCADA/HMI information conceals harmful physical-process drift. The testbed spans external threat, perimeter, DMZ, enterprise IT, supervisory control, OT control, field process, and monitoring/data-acquisition layers, supporting realistic attacker progression, telemetry interception, semantic manipulation, supervisory deception, and concealed process deviation. A hybrid cyber-physical dataset is generated from communication descriptors, protocol variables, supervisory states, controller attributes, process measurements, and cross-zone consistency indicators. For detection, CZSD-Net models SCADA, HMI, PLC, RTU, and gateway components as an industrial dependency graph and learns evolving process–perception inconsistency through relation-aware graph encoding, dissociation reasoning, and temporal fusion. Experimental evaluation shows progressive improvement over baseline graph-learning configurations. The final model achieves 97.94% testing accuracy, 97.62% precision, 97.29% recall, and 97.43% F1-score. These results show that the proposed testbed and graph-temporal framework provide an effective approach for studying and detecting stealthy cyber-physical deception in smart water IoT-SCADA environments. It also supports reproducible evaluation of detection behavior across synchronized supervisory, control, network, and physical-process observations.

Network intrusion detection systems (NIDS) increasingly rely on deep learning to cope with evolving cyber attacks, yet they typically require large amounts of labeled data per attack type and exhibit poor generalization to novel threats. Recent advances in meta-learning and few-shot learning have shown promise for alleviating data scarcity, while depthwise separable convolutions (DSC) offer a computationally efficient means of extracting fine-grained spatial–temporal features from network traffic. In this paper, we propose a meta-learning framework for few-shot network intrusion detection that combines a model-agnostic meta-learning (MAML) objective with a one-dimensional DSC backbone. Tasks are defined episodically across attack families and datasets, enabling the meta-learner to acquire an initialization that rapidly adapts to unseen attack types using only a handful of labeled flows. We derive the corresponding bilevel optimization problem, analyze the computational benefits of DSC compared with standard convolutions, and present an end-to-end architecture tailored for flow-based NIDS. Experiments on CICIDS2017, NSL-KDD and UNSW-NB15 under challenging 5-shot and 10-shot settings demonstrate consistent improvements in accuracy and macro-\(F_1\) over strong convolutional, metric-based and attention-enhanced baselines inspired by recent literature. The proposed approach reduces parameters by up to 65% relative to conventional convolutions while maintaining or improving detection performance. We conclude with a discussion of limitations and directions for integrating cross-domain adaptation and self-attention into meta-learned DSC architectures.