Human-centered approaches are vital to manage the rapid growth of artificial intelligence (AI) in higher education, where AI-driven applications can reshape teaching, research, and student engagement. This study presents the Human-Centered AI for Systematic Literature Reviews (HCAI-SLR) framework to guide educators and researchers in integrating AI tools effectively. The methodology combines AI augmentation with human oversight and ethical checkpoints at each review stage to balance automation and expertise. An illustrative example and experiments demonstrate how AI supports tasks such as searching, screening, extracting, and synthesizing large volumes of literature that lead to measurable gains in efficiency and comprehensiveness. Results show that HCAI-driven processes can reduce time costs while preserving rigor, transparency, and user control. By embedding human values through constant oversight, trust in AI-generated findings is bolstered and potential biases are mitigated. Overall, the framework promotes ethical, transparent, and robust approaches to AI integration in higher education without compromising academic standards. Future work will refine its adaptability across various research contexts and further validate its impact on scholarly practices.

The escalating scale and sophistication of cyber threats compel enterprises to urgently adopt data-driven security analytics. This systematic literature review, adhering to the PRISMA protocol, rigorously synthesizes current knowledge by analyzing 65 peer-reviewed studies (2013–2023) from six major databases on enterprise-level cybersecurity analytics. Our findings reveal a significant industry-wide transition from traditional signature-based tools towards advanced cloud-enabled, big-data and artificial intelligence-powered techniques, where machine learning and graph-based models are increasingly prominent in recent works. While large organizations in finance, Information and Communication Technology, and critical utilities spearhead adoption, dedicated research focusing on small and medium-sized enterprises (SMEs) remains notably limited. Ten thematic observations encapsulate key adoption drivers, an evolving preference for proactive and predictive security strategies, the critical role of heterogeneous log and network data, and persistent implementation challenges-notably data integration, skills shortages, and cost. Furthermore, this review identifies crucial open research avenues, including the development of real-time scalable analytics, unified policy languages, and critically needed SME-oriented solutions. Collectively, these insights provide a robust evidence base to inform future research trajectories and guide the practical deployment of effective cybersecurity analytics in diverse enterprise settings.

The rapid expansion of devices on the Internet of Things (IoTs) has led to a significant rise in IoT botnet attacks, creating an urgent need for advanced detection and classification methods. This study aims to evaluate the effectiveness of Kolmogorov-Arnold Networks (KANs) and their architectural variations in classifying IoT botnet attacks, comparing their performance with traditional machine learning and deep learning models. We conducted a comparative analysis of five KAN architectures, including Original-KAN, Fast-KAN, Jacobi-KAN, Deep-KAN, and Chebyshev-KAN, against models like Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRU). The evaluation was performed on three IoT botnet datasets: N-BaIoT, IoT23, and IoT-BotNet, using metrics such as accuracy, precision, recall, F1-score, training time, and model complexity. KAN variants consistently demonstrated robust performance, often exceeding traditional ML and DL models in accuracy and stability across all datasets. The Original-KAN variant, in particular, excelled in capturing complex, non-linear patterns inherent in IoT botnet traffic, achieving higher accuracy and faster convergence rates. Variations such as Fast-KAN and Deep-KAN offered favorable trade-offs between computational efficiency and modeling capacity, making them suitable for real-time and resource-constrained IoT environments. Kolmogorov-Arnold Networks prove to be highly effective for IoT botnet classification, outperforming conventional models and offering significant advantages in adaptability and accuracy. The integration of KAN-based models into existing cybersecurity frameworks can enhance the detection and mitigation of sophisticated botnet threats, thus contributing to more resilient and secure IoT ecosystems.

Network attacking using malware has become very popular on the Internet and in many other networks, namely Vehicular Ad-hoc Network (VANET) networks. It is required to have the model describing the malware spreading based on factors, which directly affect this process to limit its influences. In this paper, we propose a mathematical model called SEIR-S (Susceptible–Exposed–Infectious–Recovered–Susceptible) based on the characteristics of the VANET network and the well-known disease-spreading model SIR (Susceptible–Infectious–Recovered). We take into account possible behaviors of malware and provide the corresponding states to vehicles: Susceptible (S), Exposed (E), Infectious (I), Recovered (R). We evaluate the basic reproduction number ${\mathit{R}}_0$ of the model and perform a stability analysis of the proposed model. The results show that, when ${\mathit{R}}_0$ < 1, the malware spreading will gradually decrease, and, when ${\mathit{R}}_0$ > 1, that spreading cannot be extinguished. We also point out the condition that we can control the endemic in the VANET network. In addition, the correctness of the proposed model is verified using both numerical analysis and agent-based simulation on NetLogo.