With the rise and advancements of technology, digital security is facing unprecedented challenges. Traditional security measures, such as strong credentials and multi-factor authentication (MFA), are no longer sufficient to protect digital assets from attacks and unauthorized access or to effectively verify user authenticity. Zero trust architecture (ZTA) has emerged as a solution in response to these issues. Leading organizations, including Google, Forrester, Palo Alto, and CISCO, have initiated strategies to design their own deployable ZTAs to offer the perks of ZT to other organizations. It operates on various principles such as removing implicit trust, continuously authenticating users, and de-perimeterization. It provides a top-notch defense against various major cyberattacks on organizations. This study offers a comprehensive and multi-dimensional synthesis of ZTAs. It also examines the principles and implementation strategies of various ZTAs, comparing them using multiple parameters to highlight their strengths and weaknesses. In addition to the synthesis of ZTAs, the work demonstrates its practical value with sector-specific examples to make it more relevant for real-world applications. Through a detailed analysis, the study identified potential research gaps and areas in existing research that require further investigation. These insights can guide future developments in digital security. The findings aim to help organizations select and implement the most effective ZT strategies to enhance their cybersecurity posture.

Developing online educational platforms necessitates the incorporation of new intelligent procedures in order to improve long-term student experience. Presently, e-learning recommender systems rely on deep learning methods to recommend appropriate e-learning materials to the students based on their learner profiles. Fine-grained sentiment analysis (FSA) can be leveraged to enrich the recommender system. User-posted reviews and rating data are vital in accurately directing the student to the appropriate e-learning resources based on posted comments by comparable learners. In this work, a new e-learning recommendation system is proposed based on individualization and FSA. A hybrid framework is provided by integrating alternating least square (ALS) based collaborative filtering (CF) with FSA to generate an effective e-content recommendation named HCFSAR. ALS attempts to capture the learner’s latent factors based on their selections of interest to build the learner profile. Three FSA models based on attention mechanisms and bidirectional long short-term memory (bi-LSTM) are suggested and used to train twelve models in order to predict new ratings from learner-posted book reviews based on the extracted learner profile. HCFSAR used multiplication word embeddings for stronger corpus representation that were trained on a dataset generated for an educational context and showed a better accuracy of 93.39% for the best model entitled MHAM based ABHR-2 with multiplication (MHAAM), which performed better than other models. A tailored dataset that has been created by scraping reviews of different e-learning resources is leveraged to train different proposed models and validate against public datasets.

Enhancing virtual learning platforms need to adapt new intelligent mechanisms so that long-term learner experience can be improved. Sentiment Analysis gives us perception on how a specific scientific material is suitable to be recommended to the learner. It depends on the feedback of a similar learner taking many factors under consideration such as preference, knowledge level, and learning pattern. In this work, a hybrid e-learning recommendation system is proposed based on individualization and Sentiment Analysis. A new approach is provided for modelling the semantic user model based on the generated semantic matrix to capture the learner’s preferences based on their selections of interest. The extracted semantic matrix is used for text representation by utilizing ConceptNet knowledge base which relies on contextual graph and expanded terms to represent the correlation among terms and materials. On the extracted terms from semantic user model, Word Embeddings-Based-Sentiment Analysis (WEBSA) must recommend the learning materials with highest rating to the learners properly. Variant models of (WEBSA) are proposed relying on Natural Language Processing (NLP) to generate effective vocabulary representations along with the use of qualitative customized Convolutional Neural Network (CNN) for sentiment multi-classification tasks. To validate the language model, two datasets are used, a tailored dataset that has been created by scraping reviews of different e-learning resources, and a public dataset. From the experimental results, it has been found that the lowest error rate is achieved with our customized dataset, where the model named CNN-Specific-Task-CBOWBSA outperforms than others with 89.26% accuracy.