With the rise of remote work and cloud-first strategies in 2020, the traditional perimeter-based security model became increasingly obsolete. Zero Trust Architecture (ZTA), which emphasizes “never trust, always verify,” emerged as a strategic framework to mitigate evolving cybersecurity threats, especially those exacerbated by the COVID-19 pandemic. Despite its theoretical robustness, implementing Zero Trust in enterprise environments revealed several technical and organizational roadblocks, including identity-centric access control, network segmentation, legacy system compatibility, and the scalability of real-time monitoring. This paper evaluates the (...) underlying principles of Zero Trust from both cybersecurity and organizational perspectives and identifies implementation challenges through a mixed-methods approach combining quantitative threat metrics and qualitative enterprise case studies. By synthesizing insights from cybersecurity engineering, organizational behavior, and risk management, this research proposes a phased framework to streamline Zero Trust adoption in complex network environments. Findings show that tailored ZTA strategies significantly reduced insider threat surfaces and lateral movement during the rapid digital transformations of 2020, but required extensive investment in identity governance and cross-functional coordination. (shrink)

Artificial intelligence technology that recognizes, learns, infers, and responds to external stimuli has recently attracted a lot of research interest. Information in a variety of domains by fusing big data, machine learning algorithms, and computing technologies. Nowadays, practically every industry uses artificial intelligence technology, and a large number of machine learning specialists are attempting to standardize and integrate different machine learning tools so that non-experts can use them with ease in their field. In order to standardize the concepts of machine (...) learning, the researchers are also investigating autonomous machine learning and ontology construction. In this paper, we present a problem solving process and categorize common steps in autonomous machine learning problem solving as tasks. We suggest a way to model self-learning machines using a workflow of machine learning tasks. Our proposed machine learning model based on task ontology, sets up a way to group UML activities by task. It will also create and grow machine learning models on its own using rules to change common parts and structures (how elements connect and work together). (shrink)

To ensure widespread access, scalability, and sharing capabilities, applications of the Industrial Internet of Things (IIoT) leverage the cloud for storing the data streams they collect. However, the secure storage and sharing of the vast amounts of continuously generated data present significant privacy challenges, including the risk of data breaches. This paper introduces SmartCrypt, a system designed for secure data storage and sharing that facilitates analytics on encrypted time series data. SmartCrypt allows users to securely and selectively share their encrypted (...) data through an innovative symmetric homomorphic encryption method. Simulation results indicate that SmartCrypt decreases query time by 17% and enhances throughput by 9% compared to the benchmark scheme. Furthermore, SmartCrypt's scalability and flexibility make it an attractive solution for various IIoT applications. The system's performance and security features demonstrate its potential for widespread adoption. Future research directions include exploring SmartCrypt's applicability in other domains, such as healthcare and finance. (shrink)

The potential for enormous carbon emissions after fires makes peatlands a serious environmental hazard. Effective management is hampered by the labour intensive and real-time data-poor nature of conventional ground water level (GWL) monitoring in peatlands. issue study addressed issue by proposing an IoT system for real-time monitoring that uses neural network-based GWL prediction. The neural network forecasts GWL based on meteorological factors, giving the responsible party more time to take the necessary steps to lower the danger of fire in peatlands. (...) With a Root Mean Square Error (RMSE) ranging from 3.554 to 4.920, the suggested neural network shows encouraging results and guarantees 99% accuracy within a 14.760 mm range of the real GWL. This research highlights the innovative method of combining neural networks and the Internet of Things for peatland GWL prediction, providing a major breakthrough in real-time monitoring and fire risk reduction techniques. The innovation is its capacity to use basic meteorological information to anticipate GWL in real-time, even in places without the resources for traditional monitoring. (shrink)