Knowledge, Volume 5, Issue 1 (March 2025) – 6 articles

Intrusion detection is a critical component of cybersecurity, enabling timely identification and mitigation of network threats. This study proposes a novel two-stage intrusion detection framework using the CSE-CIC-IDS2018 dataset, a comprehensive and realistic benchmark for network traffic analysis. The research explores two distinct approaches: the stacked autoencoder (SAE) approach and the Apache Spark-based (ASpark) approach. Each of these approaches employs a unique feature representation technique. The SAE approach leverages an autoencoder to learn non-linear, data-driven feature representations. In contrast, the ASpark approach uses principal component analysis (PCA) to reduce dimensionality and retain 95% of the data variance. In both approaches, a binary classifier first identifies benign and attack traffic, generating probability scores that are subsequently used as features alongside the reduced feature set to train a multi-class classifier for predicting specific attack types. The results demonstrate that the SAE approach achieves superior accuracy and robustness, particularly for complex attack types such as DoS attacks, including SlowHTTPTest, FTP-BruteForce, and Infilteration. The SAE approach consistently outperforms ASpark in terms of precision, recall, and F1-scores, highlighting its ability to handle overlapping feature spaces effectively. However, the ASpark approach excels in computational efficiency, completing classification tasks significantly faster than SAE, making it suitable for real-time or large-scale applications. Both methods show strong performance for distinct and well-separated attack types, such as DDOS attack-HOIC and SSH-Bruteforce. This research contributes to the field by introducing a balanced and effective two-stage framework, leveraging modern machine learning models and addressing class imbalance through a hybrid resampling strategy. The findings emphasize the complementary nature of the two approaches, suggesting that a combined model could achieve a balance between accuracy and computational efficiency. This work provides valuable insights for designing scalable, high-performance intrusion detection systems in modern network environments. Full article

Sustainable knowledge sharing among mathematics and science teachers is imperative to improve the ability of such teachers to enhance the way information is transferred to learners. South Africa ranked 37th out of 42 countries in an assessment to determine the ability of high school learners to conduct mathematics and science. There is, therefore, an urgent need to investigate how teachers can be empowered to enhance their ability to transfer knowledge of mathematics and science to improve the ability of learners to engage in these subjects. A post-positivist paradigm and quantitative survey design were employed to identify ways of knowledge sharing that will enhance the ability of teachers to transfer knowledge of mathematics and science to learners. The findings identified key barriers to knowledge sharing, including the role of school management in fostering a culture of knowledge exchange, time management, and limited opportunities for professional development. Based on the findings of the research, a framework is proposed to encourage knowledge sharing, which may ultimately improve teaching practices and learner outcomes in mathematics and science. Full article

ChatGPT, or Chat Generative Pre-trained Transformer, developed by OpenAI, is a versatile chatbot known for generating human-like text responses. Since its launch in November 2022, it has sparked interest and debate. This bibliometric study aimed to explore ChatGPT-related publications using the Web of Science database from 2023 to June 2024. Original articles in English were retrieved on 24 June 2024, using the topic field “ChatGPT”. Citation records were analyzed using bibliometrix 4.1 and VOSviewer 1.6.20. Between January 2023 and 24 June 2024, 3231 original articles on ChatGPT were published in 1404 journals, with an average citation rate of 5.6 per article. The United States led with 877 articles, followed by China and India. The University of California System, Harvard University, and the State University System of Florida were the most prolific institutions. Keyword co-occurrence network analysis revealed the interdisciplinary nature of ChatGPT research, particularly contributions in healthcare, education, and technology. In conclusion, this bibliometric analysis identified critical areas of ChatGPT research focus, such as applications in educational settings and its ethical implications. These findings are crucial for fostering further advancements that leverage ChatGPT’s capabilities while mitigating its risks. Full article

Epistemology and technology have been working in synergy throughout history. This relationship has culminated in large language models (LLMs). LLMs are rapidly becoming integral parts of our daily lives through smartphones and personal computers, and we are coming to accept the functionality of LLMs as a given. As LLMs become more entrenched in societal functioning, questions have begun to emerge: Are LLMs capable of real understanding? What is knowledge in LLMs? Can knowledge exist independently of a conscious observer? While these questions cannot be answered definitively, we can argue that modern LLMs are more than mere symbol-manipulators and that LLMs in deep neural networks should be considered capable of a form of knowledge, though it may not qualify as justified true belief (JTB) in the traditional definition. This deep neural network design may have endowed LLMs with the capacity for internal representations, basic reasoning, and the performance of seemingly cognitive tasks, possible only through a compressive but generative form of representation that can be best termed as knowledge. In addition, the non-symbolic nature of LLMs renders them incompatible with the criticism posed by Searle’s “Chinese room” argument. These insights encourage us to revisit fundamental questions of epistemology in the age of LLMs, which we believe can advance the field. Full article

The significance of knowledge management in the tourism accommodation sector is increasingly vital due to rapid market changes and intense competition. Although the value of identifying and implementing critical success factors (CSFs) for knowledge management is widely recognized in the sector, there is still a lack of comprehensive understanding and practical application of these factors. This study employs the decision-making trial and evaluation laboratory (DEMATEL) methodology to systematically identify and analyze the interrelationships among these CSFs. The findings reveal a complex web of dependencies within this network. Specifically, leadership commitment and support is identified as the most influential CSF, acting as a fundamental element that enables the successful adoption and integration of knowledge management initiatives. Additionally, strategic alignment and a supportive organizational culture are crucial, working synergistically to ensure that knowledge management initiatives are aligned with overarching organizational goals and create an environment that encourages change and collaboration. Furthermore, the study highlights a mutually reinforcing relationship between knowledge processes, governance, and employee training. This relationship suggests that strong governance structures and clearly defined knowledge processes facilitate and improve the effectiveness of employee training programs while also creating a continuous improvement cycle where improved training further refines governance and knowledge processes. Moreover, the study highlights the integration of the ISO 30401:2018 standard as a systematic framework to support these CSFs, providing a structured approach to improve knowledge management systems. By mapping the cause-and-effect relationships among the identified CSFs, this research offers practical insights for industry professionals to effectively prioritize and address these factors. Full article

Turing’s instability has been widely introduced to explain the formation of several biological and ecological patterns, such as the skin patterning of fish or animals, wings of butterflies, pigmentation, and labyrinth patterns of the cerebral cortex of mammals. Such a mechanism may occur in the ecosystem due to the differential diffusion dispersal that happen if one of the constituent species results in the activator or the prey, showing a tendency to undergo autocatalytic growth. The diffusion of the constituent species activator is a random mobility function called passive diffusion. If the other species in the system (the predator/inhibitor) disperses sufficiently faster than the activator, then the spatially uniform distribution of species becomes unstable, and the system will settle into a stationary state. This paper introduced Turing’s mechanism in our reaction–taxis–diffusion model to simulate the harmful algal bloom (HAB) pattern. A numerical approach, the Runge–Kutta method, was used to deal with this system of reaction–taxis–diffusion equations, and the findings were qualitatively compared to the aerial patterns obtained by a drone flying over Torment Lake in Nova Scotia (Canada) during the bloom season of September 2023. Full article