In this paper, we aim to explore the possibility of the Transformer model in detecting the spam Short Message Service (SMS) messages by proposing a modified Transformer model that is designed for detecting SMS spam messages. The evaluation of our proposed spam Transformer is performed on SMS Spam Collection v.1 dataset and UtkMl’s Twitter Spam Detection Competition dataset, with the benchmark of multiple established machine learning classifiers and state-of-the-art SMS spam detection approaches. In comparison to all other candidates, our experiments on SMS spam detection show that the proposed modified spam Transformer has the optimal results on the accuracy, recall, and F1-Score with the values of 98.92%, 0.9451, and 0.9613, respectively. Besides, the proposed model also achieves good performance on the UtkMl’s Twitter dataset, which indicates a promising possibility of adapting the model to other similar problems.

The Metaverse, positioned as the next frontier of the Internet, has the ambition to forge a virtual shared realm characterized by immersion, hyper-spatiotemporal dynamics, and self-sustainability. Recent technological strides in AI, Extended Reality, 6G, and blockchain propel the Metaverse closer to realization, gradually transforming it from science fiction into an imminent reality. Nevertheless, the extensive deployment of the Metaverse faces substantial obstacles, primarily stemming from its potential to infringe on privacy and be susceptible to security breaches, whether inherent in its underlying technologies or arising from the evolving digital landscape. Metaverse security provisioning is poised to confront various foundational challenges owing to its distinctive attributes, encompassing immersive realism, hyper-spatiotemporally, sustainability, and heterogeneity. This article undertakes a comprehensive study of the security and privacy challenges facing the Metaverse, leveraging machine learning models for this purpose. In particular, our focus centers on an innovative distributed Metaverse architecture characterized by interactions across 3D worlds. Subsequently, we conduct a thorough review of the existing cutting-edge measures designed for Metaverse systems while also delving into the discourse surrounding security and privacy threats. As we contemplate the future of Metaverse systems, we outline directions for open research pursuits in this evolving landscape.

Internet of Drones (IoD) offers several advantages, such as enabling ground communications in areas with connectivity limitations due to physical obstacles. However, IoD also introduces new security vulnerabilities. For instance, an attacker could launch a drone capture attack to obtain confidential credentials and inject counterfeit information into the IoD network, jeopardizing its smooth operation. In such scenarios, authentication plays a crucial role in ensuring security. Unfortunately, the existing authentication frameworks are insufficient to protect against attacks in the IoD environment. In this article, we propose a secure and anonymous authentication framework called SAAF-IoD for the IoD. SAAF-IoD utilizes a chaotic map in combination with symmetric encryption and hash functions. It allows external users of the IoD network to establish a session key with the deployed drone by performing mutual authentication with the Ground-Station Server (GS). The proposed SAAF-IoD framework provides resistance against privileged insider attacks and GS bypass attacks. We validate the resilience of SAAF-IoD against various security attacks through both formal and informal analyses. Furthermore, we compare the efficiency of the proposed framework with a benchmark scheme in terms of communication and computational costs. Our results demonstrate that SAAF-IoD requires fewer resources than the relevant schemes while offering enhanced security features.

Due to the short communication range, users are selected as relays to support multi-hop transmission for content sharing/downloading in D2D communications. In this letter, we first exploit social relationships from interacts and contributions, and then formulate an optimal relay selection problem to enhance cooperative multi-hop D2D communications. A Power-efficient and Social-aware Relay Selection (PSRS) algorithm is provided to solve the above problem. Simulation results show that our method has the lowest average power consumption compared to the existing methods.

Federated learning offers a framework for developing local models across institutions while safeguarding sensitive data. This paper introduces a novel approach for heart disease prediction using the TabNet model, which combines the strengths of tree-based models and deep neural networks. Our study utilizes the Comprehensive Heart Disease and UCI Heart Disease datasets, leveraging TabNet’s architecture to enhance data handling in federated environments. Horizontal federated learning was implemented using the federated averaging algorithm to securely aggregate model updates across participants. Blockchain technology was integrated to enhance transparency and accountability, with smart contracts automating governance. The experimental results demonstrate that TabNet achieved the highest balanced metrics score of 1.594 after 50 epochs, with an accuracy of 0.822 and an epsilon value of 6.855, effectively balancing privacy and performance. The model also demonstrated strong accuracy with only 10 iterations on aggregated data, highlighting the benefits of multi-source data integration. This work presents a scalable, privacy-preserving solution for heart disease prediction, combining TabNet and blockchain to address key healthcare challenges while ensuring data integrity.