Purpose Wafer foundries face numerous challenges in localizing the semiconductor supply chain, necessitating various measures to overcome these challenges. Given their limited funding, time and resources, these measures require prioritization. Design/methodology/approach To prioritize these measures, this study proposes a dynamic fuzzy compromise planning (DFCP) approach, which dynamically allocates resources across a limited budget based on the priority of measures within each time period. The DFCP approach embeds a fuzzy technique for order preference by similarity to ideal solution mechanism into a fuzzy mixed binary-nonlinear programming model. Findings The DFCP approach has been applied to a real-world case. Experimental results demonstrated that the DFCP approach satisfied the three objectives of wafer foundries, while existing methods either over-concentrated resources on a few possible measures or produced discontinuous planning fragments. Originality/value Semiconductor supply chain localization is undoubtedly one of the most advanced manufacturing models in the semiconductor industry, yet it has received little discussion to date. The DFCP approach combines multi-criteria decision-making practices with fuzzy optimization models, which is quite different from most existing fuzzy compromise programming methods.
Purpose This study aims to develop and validate a quantum-enhanced behavioral security framework that integrates a lightweight quantum obfuscation layer into classical intrusion detection systems (IDSs). The primary objective is to harden these models against post-quantum threats, specifically model extraction and feature inversion attacks, while maintaining high detection accuracy across both traditional machine learning and deep learning architectures. Design/methodology/approach A quantum feature obfuscation layer was designed using 6-qubit parameterized circuits with angle encoding and variational ansatz to nonlinearly transform behavioral features. This layer was integrated with classical classifiers (random forest, support vector machine, logistic regression) and a deep learning model multi-layer perceptron (MLP). The framework was evaluated on UNSW-NB15 and NSL-KDD datasets using Qiskit Aer simulators in Google Colab, measuring accuracy, precision, recall, F1-score, model extraction error (MEE) and feature inversion error (FIE). Findings Quantum-assisted models maintained detection performance comparable to classical baselines, with accuracy degradation of 0.7% across all architectures. Security resilience significantly improved, with a 3–4× increase in both MEE and FIE, indicating substantially enhanced resistance to model theft and feature reconstruction. The framework demonstrated improved noise tolerance under Gaussian perturbations. The MLP achieved 92.5% accuracy (classical) versus 91.8% (quantum-assisted), with 3.3× and 3.5× improvements in MEE and FIE, respectively. Research limitations/implications The evaluation was conducted entirely on quantum simulators, not capturing real hardware noise, decoherence and fidelity limitations. The threat model assumes secrecy of quantum circuit parameters, representing a form of model-level security through obscurity. Scalability is constrained by exponential simulation costs, limiting current experiments to 6 qubits. Only feedforward neural networks (MLP) were tested; advanced architectures like CNNs, LSTMs and transformers require future validation. The framework’s resilience against adversaries with partial knowledge of the ansatz structure remains unexplored. Practical implications The framework provides a pragmatic, classifier-agnostic defense layer deployable on freely accessible cloud platforms (Google Colab) without specialized quantum hardware. With only 15–25 ms inference overhead and 40–60% training overhead, it offers viable post-quantum hardening for security-critical applications. Resource-constrained environments such as edge computing nodes and IoT deployments can benefit from this approach. The demonstration that meaningful security gains (3–4× improvement) are achievable using only 6 qubits lowers the barrier for organizations to adopt quantum-assisted security measures today. Social implications As quantum computing threatens classical cryptography, protecting behavioral analytics becomes crucial for critical infrastructure, financial systems and healthcare networks. This research contributes to building resilient cyber defense mechanisms that protect sensitive data and privacy even when encryption is compromised. By democratizing access to quantum-enhanced security through cloud-based simulation platforms, the framework helps bridge the gap between institutions with varying resources, promoting more equitable cybersecurity preparedness for the post-quantum era across both developed and developing nations. Originality/value This work shifts the focus of quantum-enhanced security from pure cryptographic replacement to model-hardening, harnessing quantum state complexity as a defensive mechanism rather than pursuing quantum advantage for classification speed. It introduces a novel quantum obfuscation layer specifically designed for IDS, validated across both traditional ML and deep learning architectures. The consistent security improvements (3–4×) across classifier types confirm the approach is classifier-agnostic. The Colab-based implementation ensures reproducibility and accessibility, providing a practical foundation for future quantum-aware cyber defense research and deployment.
PurposeWith the rapid proliferation of Internet-connected devices, software-defined networking (SDN) has become an attractive paradigm for efficient network management compared to traditional network infrastructure. Nevertheless, SDN networks remain susceptible to distributed denial-of-service (DDoS) attacks, significantly degrading network performance and reliability. This research seeks to overcome the current limitation posed by the absence of realistic SDN-based datasets for DDoS detection and proposes an enhanced detection methodology tailored explicitly for SDN networks.Design/methodology/approachThis research introduces SDN-DDoS2025, a novel dataset developed by adapting the CIC-DDoS2019 dataset to an SDN environment while preserving flow- and traffic-level characteristics unique to SDN. Comprehensive exploratory data analysis (EDA) is performed to thoroughly investigate feature distributions, trends and correlations within the dataset. Furthermore, a hybrid deep learning model integrating one-dimensional convolutional neural networks with long short-term memory layers (CNN-LSTM) is proposed, demonstrating enhanced efficiency and effectiveness in detecting DDoS attacks.FindingsThe experimental results reveal that the proposed CNN-LSTM hybrid model outperforms recent benchmark methods on both the original CIC-DDoS2019 and the newly introduced SDN-DDoS2025 datasets. The model attains superior detection accuracy and demonstrates robust generalization capabilities, handling real-world SDN traffic patterns.Originality/valueThis research presents the first SDN-specific dataset derived from the CIC-DDoS2019 benchmark, incorporating realistic SDN-oriented features and proposes a robust deep learning architecture specifically designed for effective DDoS detection in SDN contexts. By integrating dataset generation, comprehensive EDA and advanced deep learning modeling, this research addresses a significant research gap within the SDN security domain.
Purpose - This paper aims to present a modular pipeline approach by combining extractive and abstractive summarisation methods in order to improve the accuracy of automatic text summarisation (ATS). Design/methodology/approach - In our framework we have used LangChain [20], a large language model integration platform, to orchestrate an extractive algorithm (LexRank) and an abstractive algorithm (Bidirectional and Auto-Regressive Transformers (BART)). LexRank identifies key sentences from the input text, ensuring core information is retained without redundancy, BART is used for refining these extracted sentences into human-like, concise summaries. Findings - We evaluated the performance of our framework by using CNN/Daily Mail, Newsroom and XSum datasets, which are widely used benchmark for summarisation tasks. Evaluation results demonstrate that this modular approach improves accuracy of generated summaries in two ways, (1) word level similarities between reference summary and generated summary and (2) semantic similarity between reference summary and generated summary. Research limitations/implications - In our experiments, we used datasets that have short length documents (within 7,000 characters). We need to perform more experiments with long documents to evaluate the robustness of our pipeline. We have not investigated error propagation from extractive phase to abstractive phase that may have an impact on the performance of our framework. Originality/value - The primary contribution of this study is the development and evaluation of a modular pipeline combining LexRank, an extractive summarisation technique, and BART, an advanced abstractive model, using LangChain. The results indicate that the hybrid approach outperforms traditional single-model methods and other existing hybrid models, as evidenced by higher Recall-Oriented Understudy for Gisting Evaluation and BERTScores across various examples.
PurposeThis study aims to develop a data-driven Deep Koopman-based model predictive control (DK-MPC) framework for real-time trajectory tracking and stabilization of quadrotor systems. The objective is to overcome the computational limitations of conventional nonlinear model predictive control (NMPC) while preserving high-accuracy control performance.Design/methodology/approachA deep neural network-based Koopman operator is employed to map the nonlinear quadrotor dynamics into a globally linear latent space, enabling the formulation of a computationally efficient linear model predictive control (MPC) problem. The Koopman embeddings are trained using the publicly available WaveLab Pelican dataset to capture the coupled and nonlinear dynamics of the quadrotor. The proposed DK-MPC framework is evaluated through numerical simulations involving point stabilization and trajectory tracking tasks, including previously unseen helical trajectories. Performance is assessed in terms of tracking accuracy, computational efficiency and real-time feasibility, with comparisons made against conventional NMPC.FindingsSimulation results demonstrate that the proposed DK-MPC framework achieves high-precision control with a coefficient of determination (R2) of 99%. The method requires approximately 10% of the computation time per control step compared to NMPC. For a prediction horizon of H = 50, DK-MPC maintains real-time feasibility with an average computation time of 15 ms per control step, while NMPC frequently fails to converge within the required time constraints. These findings confirm the effectiveness of the Koopman-based linear embedding in reducing computational burden without compromising control accuracy.Originality/valueThe study presents a scalable and computationally efficient integration of deep Koopman operator learning with MPC for quadrotor systems. By combining data-driven nonlinear system representation with linear MPC optimization, the proposed framework bridges the gap between advanced learning-based modeling and real-time predictive control implementation.
