This paper examines the impact of stringent regulations on personal data protection on customer perception of data security and online shopping behavior. In the context of the rapidly expanding e-commerce landscape, ensuring the security of personal data is a complex and crucial task. The study of several legal frameworks, including Malaysia’s compliance with EU regulations and Indonesia’s Personal Data Protection Law, provides valuable insights into consumer data protection. The challenges of balancing data safeguarding and unrestricted movement and tackling misuse by external entities are significant and require careful consideration. This research elucidates the pivotal role of trust in e-commerce environments and the deployment of innovative e-commerce models designed to minimize personal data sharing. By integrating advanced privacy-enhancing technologies and adhering to stringent regulatory standards such as the GDPR, this study demonstrates effective strategies for robust data protection. The paper contributes to the academic discourse by providing a comprehensive framework that synergizes legal, technological, and procedural elements to fortify data security and enhance consumer trust in digital marketplaces. This approach aligns with international data protection standards and offers a pragmatic blueprint for achieving sustainable data security in e-commerce.

Cybersecurity threats are becoming more intricate, requiring preemptive actions to safeguard digital assets. This paper examines the function of honeypots as critical instruments for threat detection, analysis, and mitigation. A novel methodology for comparative analysis of honeypots is presented, offering a systematic framework to assess their efficacy. Seven honeypot solutions, namely Dionaea, Cowrie, Honeyd, Kippo, Amun, Glastopf, and Thug, are analyzed, encompassing various categories, including SSH and HTTP honeypots. The solutions are assessed via simulated network attacks and comparative analyses based on established criteria, including detection range, reliability, scalability, and data integrity. Dionaea and Cowrie exhibited remarkable versatility and precision, whereas Honeyd revealed scalability benefits despite encountering data quality issues. The research emphasizes the smooth incorporation of honeypots with current security protocols, including firewalls and incident response strategies, while offering comprehensive insights into attackers’ tactics, techniques, and procedures (TTPs). Emerging trends are examined, such as incorporating machine learning for adaptive detection and creating cloud-based honeypots. Recommendations for optimizing honeypot deployment include strategic placement, comprehensive monitoring, and ongoing updates. This research provides a detailed framework for selecting and implementing honeypots customized to organizational requirements.

In the past twenty years, the IT industry has moved away from using physical servers for workload management to workloads consolidated via virtualization and, in the next iteration, further consolidated into containers. Later, container workloads based on Docker and Podman were orchestrated via Kubernetes or OpenShift. On the other hand, high-performance computing (HPC) environments have been lagging in this process, as much work is still needed to figure out how to apply containerization platforms for HPC. Containers have many advantages, as they tend to have less overhead while providing flexibility, modularity, and maintenance benefits. This makes them well-suited for tasks requiring a lot of computing power that are latency- or bandwidth-sensitive. But they are complex to manage, and many daily operations are based on command-line procedures that take years to master. This paper proposes a different architecture based on seamless hardware integration and a user-friendly UI (User Interface). It also offers dynamic workload placement based on real-time performance analysis and prediction and Machine Learning-based scheduling. This solves a prevalent issue in Kubernetes: the suboptimal placement of workloads without needing individual workload schedulers, as they are challenging to write and require much time to debug and test properly. It also enables us to focus on one of the key HPC issues—energy efficiency. Furthermore, the application we developed that implements this architecture helps with the Kubernetes installation process, which is fully automated, no matter which hardware platform we use—x86, ARM, and soon, RISC-V. The results we achieved using this architecture and application are very promising in two areas—the speed of workload scheduling and workload placement on a correct node. This also enables us to focus on one of the key HPC issues—energy efficiency.

Judicial decision-making in continental law systems requires carefully evaluating complex, interdependent evidence while ensuring consistency and fairness. This study investigates the application of Bayesian networks in structuring legal evidence within an AI-based decision support system. The primary research objective is to assess the enhancement of transparency, minimization of cognitive overload, and reduced bias in judicial processes through probabilistic reasoning. The proposed system dynamically updates outcome probabilities as new evidence is introduced, enabling real-time monitoring of case likelihoods. AI-generated recommendations are aligned with judicial precedents by integrating Conditional Probability Tables (CPTs) and historical case data in this adaptive approach. The interpretability and effectiveness of Bayesian inference in legal decision support are analyzed methodologically, emphasizing its capacity to refine probability distributions in response to evolving courtroom inputs. The findings address a key research gap by demonstrating how structured AI-driven heuristics can supplement judicial reasoning while maintaining decision accountability and transparency. The system is suggested to enhance consistency and fairness in legal judgments while preserving judicial autonomy. This study contributes to the growing intersection of AI and legal decision-making, with an emphasis placed on the role of machine learning in supporting judicial heuristics while maintaining procedural integrity.

The Zero Trust Architecture (ZTA) security system follows the “never trust, always verify” principle. The process constantly verifies users and devices trying to access resources. This paper describes how Microsoft Azure uses ZTA to enforce strict identity verification and access rules across the cloud environment to improve security. Implementation takes time and effort. Azure’s extensive services and customizations require careful design and implementation. Azure administrators need help navigating and changing configurations due to its complex user interface (UI). Each Azure ecosystem component must meet ZTA criteria. ZTAs comprehensive policy definitions, multi-factor and passwordless authentication, and other advanced features are tested in a mid-size business scenario. The document delineates several principal findings concerning the execution of Azure’s ZTA within mid-sized enterprises. Azure ZTA significantly improves security by reducing attack surfaces via ongoing identity verification, stringent access controls, and micro-segmentation. Nonetheless, its execution is resource-demanding and intricate, necessitating considerable expertise and meticulous planning. A notable disparity exists between theoretical ZTA frameworks and their practical implementation, characterized by disjointed management interfaces and user fatigue resulting from incessant authentication requests. The case studies indicate that although Zero Trust Architecture enhances organizational security and mitigates risks, it may disrupt operations and adversely affect user experience, particularly in hybrid and fully cloud-based settings. The study underscores the necessity for customized configurations and the equilibrium between security and usability to ensure effective ZTA implementation.

This paper thoroughly assesses the ARM and RISC-V architectures in the context of high-performance computing (HPC). It includes an analysis of Docker and Kubernetes integration. Our study aims to evaluate and compare these systems’ performance, scalability, and practicality in a general context and then assess the impact they might have on special use cases, like HPC. ARM-based systems exhibited better performance and seamless integration with Docker and Kubernetes, underscoring their advanced development and effectiveness in managing high-performance computing workloads. On the other hand, despite their open-source architecture, RISC-V platforms presented considerable intricacy and difficulties in working with Kubernetes, which hurt their overall effectiveness and ease of management. The results of our study offer valuable insights into the practical consequences of implementing these architectures for HPC, highlighting ARM’s preparedness and the potential of RISC-V while acknowledging the increased complexity and significant trade-offs involved at this point.