By Industry 4.0 has resulted in the prevalence of smart factories, in which there is a close relationship between computation, communication, and physical processes through cyber-physical systems (CPS). Even though productivity, flexibility, and efficiency are improved under this integration, new security challenges that have never been experienced previously and go beyond the traditional cybersecurity boundaries are created. Cyber-physical security (CPSec) is the provision of defensive of the cyber components, as well as physical processes against malicious attacks, failures, and system anomalies. The paper is a systematic and multifaceted review of the new trends in cyber-physical security of smart factories. It looks at the threat landscapes, attack vectors, architecture weaknesses, and intersection of the information technology (IT) and operational technology (OT) worlds. In addition, the article evaluates the innovative defense systems such as artificial intelligence-based intrusion detection systems, blockchain-based trust management systems, zero-trust-based architectures, and security validation using digital twins. An adaptable, resilient, and real-time threat response-focused layered security approach is suggested to smart manufacturing settings. Recent experimental data on the role of advanced CPSec strategies is presented based on the considerations of industrial case studies and simulations. Lastly, the paper identifies open research problems and future directions requirements in order to establish robust, scale up and smart cyber-physical security systems in next generation smart factories.
Hiroshi Tanaka, Yuki Nakamura· International Journal of Mod...· 0 citations
The increasing complexity, scale, and release frequency of contemporary software systems have exposed limitations in conventional testing practices, particularly in exhaustive test execution, regression validation, and early defect identification. This research proposes an intelligent framework that integrates artificial intelligence (AI)-based test automation with software defect prediction to establish a proactive quality-engineering process. The proposed framework combines requirement and code analysis, automated test generation, execution prioritization, defect-risk estimation, feedback-driven model refinement, and quality reporting within a unified architecture. The methodological foundation is a conceptual synthesis of AI-driven test automation principles, with particular emphasis on automation, intelligent prioritization, and predictive quality assurance as discussed by Ramamurthy (2023). The supplied reference corpus also demonstrates how intelligent sensing, pattern recognition, resource optimization, and data-driven classification can conceptually inform automated quality-monitoring architectures, although most of these studies originate outside software testing. The proposed model therefore treats cross-domain evidence as methodological inspiration rather than direct empirical validation. The analysis indicates that combining predictive defect-risk scores with automated test selection can potentially reduce redundant testing, concentrate computational resources on high-risk software components, and improve feedback speed. However, model reliability depends on historical defect data, feature quality, distributional stability, explainability, and integration with existing development pipelines. The framework contributes a structured basis for AI-assisted software quality engineering while identifying empirical validation, benchmark datasets, and explainable prediction as priorities for future research.
Haruto Tanaka, Yuki Nakamura· Frontiers in Emerging Multid...· 0 citations