Wireless Sensor Networks (WSNs) are integral to mission-critical applications, including environmental monitoring, smart infrastructure, and healthcare. However, they are particularly vulnerable to denial-of-service (DoS) attacks, which can deplete the node's energy and disrupt communication. This study examines the effectiveness of various machine learning algorithms in enhancing intrusion detection within WSNs, focusing on balancing detection accuracy and computational efficiency. Utilising the Network Simulator-2 (NS-2) generated WSN-DS dataset, seven algorithms—K-Nearest Neighbours (KNN), Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Stacking Classifier, AdaBoost, and Artificial Neural Network (ANN)—were implemented and evaluated. The experimental results indicate that AdaBoost achieved the highest overall performance, with an accuracy of 99.7%, ROC-AUC of 0.996, and detection speed of 1.6 min, underscoring its suitability for real-time intrusion detection. Stacking and Random Forest also demonstrated high accuracy (99.7% and 99.6%, respectively) but required slightly longer detection times of 7.07 and 7.33 min, respectively. In contrast, KNN exhibited the longest detection time (86.2 min) due to its high computational overhead, whereas Naïve Bayes was the fastest (0.02 min) but had lower precision (0.757) and F1-score (0.771). AdaBoost demonstrated superior detection accuracy, efficiency, and adaptability under constrained WSN conditions, outperforming all other algorithms across multiple performance metrics. These findings offer a practical benchmark for developing lightweight, high-performance intrusion detection systems in resource-limited wireless sensor environments, thereby enhancing the resilience and reliability of next-generation WSN infrastructures.

Cloud computing can be revolutionized by quantum computing which will offer the world more computational power than has ever been seen to solve complex issues. Quantum computing coupled with cloud computing enables the remote access to quantum resources, thus greatly minimizing the cost, technical, and operational difficulties of having quantum hardware owned and maintained in the field. The integration makes large-scale data processing, cryptography, and optimization tasks as well as new applications in artificial intelligence efficient in terms of their computation. This work is a review of the existing approaches, system, and systems to quantum cloud computing, the main algorithms, software applications, implementation plans, and real-life examples. We find that quantum cloud computing provides significant enhancements in computational speed and parallelism, scalability, as well as provides the capability to process data securely and to execute quantum circuits remotely. However, there are still a few obstacles such as stability of qubits, error correction, noise reduction, and effective resource utilization, which restrict the practical use of quantum cloud services. The findings indicate that, irrespective of these challenges, quantum computing with the use of cloud computing platforms offers meaningful potentials to scientific discovery, business, and an AI-based innovation. The paper wraps up by noting that further research should be done to enhance the reliability of quantum hardware, optimize quantum algorithms, and design quantum cloud computing security systems, enabling quantum cloud computing to be adopted more broadly as a more transformative model of computation and ensuring that quantum cloud computing can grow sustainably.

The growing number of cyber threats has made the protection of sensitive data critical. This work presents a solution integrating the Trusted Platform Module (TPM) with AES-CBC and RSA cryptography to mitigate threats like unauthorized key access and data tampering. The architecture uses the TPM as a hardware root of trust and implements a secure device authentication process using the TPM’s Endorsement Key (EK). To evaluate its practical viability, we conducted comparative experiments on multiple hardware configurations, measuring the performance impact of the TPM on encryption and decryption tasks for files up to 1GB. Our findings show a clear performance trade-off: TPM integration introduces a measurable overhead that is most significant on lower-end hardware and for smaller files. As file size increases, the relative performance penalty diminishes, though the absolute overhead grows. For instance, decryption operations consistently showed less performance variability than encryption. The results demonstrate that the solution effectively enhances security through hardware-based key isolation, and we conclude that the observed performance cost is a predictable and justifiable price for the robust protection offered against modern cyber threats.

In the dynamic landscape of cybersecurity, safeguarding computer networks against persistent malicious threats is paramount. Intrusion Detection Systems are crucial in this context by monitoring network traffic for unau-thorized access. While the integration of Machine Learning and Deep Learning has significantly advanced intrusion detection, the persistent challenge lies in effectively detecting minority attack classes. This study introduces an innovative approach that combines SHapley Additive exPlanations(SHAP) for feature selection and Deep Neural Networks(DNN) to enhance the performance of intrusion detection systems, particularly focusing on minority attack classes in the NSL-KDD dataset. Applied to a Random Forest classifier using a balanced dataset, SHAP provides valuable insights into feature importance, refining the feature set for seamless integration into a DNN architecture. Employing the NSL-KDD dataset, the research concentrates on elevating the detection accuracy for User-to-Root attack and Root-to-Local attacks. The results showcase a notable improvement in performance along with a reduction in computational time compared to using all the available features. A key emphasis of the study is on detecting all attack types without compromising the F1-score. An in-depth analysis of the initial set of 41 features identifies 30 as crucial for effective intrusion detection. On the imbalanced dataset, SHAP-based feature reduction improved the overall F1-score in multiclass classification from 86% to 91% by reducing training time by 8.86%, confirming that SHAP can lower complexity without sacrificing accuracy. However, several minority attacks remained undetected due to their extremely low representation. Additional experiments with oversampled data confirm that SHAP continues to provide efficiency gains while enabling robust detection of rare attack classes. These findings demonstrate that SHAP-based feature selection improves efficiency in IDS and has strong potential for minority attack detection if data scarcity is addressed. This research not only contributes to the enhancement of IDS capabilities but also highlights the importance of meticulous feature selection in achieving comprehensive and efficient intrusion detection.

Global supply chains are increasingly characterized by complexity, uncertainty, and vulnerability to disruptions, creating a pressing need for intelligent, adaptive systems that support decentralized decision-making and real-time control. This paper develops a new framework that integrates Multi-Agent Systems (MAS) with Case-Based Reasoning (CBR) to address these challenges. The model leverages autonomous agents representing suppliers, manufacturers, distributors, retailers, and coordinators that negotiate through defined protocols while embedding CBR mechanisms to retrieve and adapt historical supply chain cases for enhanced responsiveness. An optimization layer, guided by both agent heuristics and case-driven initial solutions, targets key objectives such as cost minimization, lead-time reduction, and resilience improvement. Simulation experiments were conducted under both static and dynamid environments with disruptions including supplier failures and demand fluctuations. Results demonstrate that the proposed framework achieves convergence up to 34- 41% faster than heuristic-only baselines (p<0.05) and sustains solution quality with supply chain sizes increasing from 50 to 500 agents, indicating near-linear scalability. Comparative analysis further highlights adaptability in dynamic contexts and robustness under uncertainty. A case study illustrates practical deployment and validates its effectiveness. The findings provide evidence of a powerful synergy between MAS and CBR, with implications for next-generation supply chain intelligence.