• Proposes SAGE-Net, a multimodal framework that models sarcasm as latent semantic incongruity. • Introduces a cross-gating mechanism using cosine similarity to filter semantically misaligned modal features. • Employs a hierarchical co-attention fusion strategy inspired by latent factor decomposition. • Integrates stylistic, visual, and textual cues to capture implicit sarcastic signals beyond surface meaning. • Enhances modality-level interpretability through gated and attention-based representations. Detecting sarcasm in social media is critical for enhancing sentiment analysis, content moderation, and online discourse understanding. This study proposes SAGE-Net (Sarcasm-Aware Gated Encoding Network). Unlike prior multimodal models that indiscriminately fuse all inputs, SAGE-Net introduces three innovations tailored for sarcasm: (1) a semantic gating mechanism that filters visually inconsistent text-image pairs before fusion; (2) a dedicated stylistic encoder (Hash-BERT) that treats hashtags and emojis as a separate modality; and (3) a hierarchical attention module that produces interpretable modality importance scores. Leveraging a publicly available multimodal Twitter dataset, the model extracts contextual text features using a domain-adapted BERT encoder, stylistic cues via a dedicated Hash-BERT model, and visual information through a ResNet-152 backbone. To address modality-level inconsistencies, a cross-gating mechanism evaluates alignment between text and image modalities, filtering noisy features using cosine similarity. These filtered embeddings are fused using cross- and co-attention modules, followed by a hierarchical attention mechanism that adaptively weighs fused modalities based on relevance. This fusion strategy aligns with latent factor decomposition principles to minimize semantic redundancy and amplify sarcastic cues. Extensive experiments demonstrate that SAGE-Net surpasses existing baseline models across multiple metrics, achieving a high F1-score while maintaining robustness to ambiguous and noisy inputs. Furthermore, an ablation study validates the contribution of each component—including the gating threshold, stylistic encoder, and hierarchical attention module—to overall model performance. The proposed approach provides a scalable and interpretable solution for sarcasm detection in real-world multimodal communication scenarios.

ABSTRACT Federated Learning (FL) has emerged as an innovative approach for distributed neural networks, allowing multiple clients to collaboratively train a model without centralising their data, thus preserving decentralisation and data privacy. This review provides a comprehensive discussion of FL's core concepts, including its components, key challenges, and distinctions from traditional machine learning. The paper outlines the various types of FL, highlighting applications in privacy‐sensitive fields like healthcare and finance. It also addresses recent advancements in self‐supervised learning, personalisation, and multi‐modal applications within FL, as well as the integration of blockchain technology for enhanced privacy. Key advantages of FL are discussed, such as reduced communication overhead through the transmission of model parameters instead of raw data, which minimises network load and enhances privacy protection. Furthermore, the paper explores emerging questions for FL development, including scalability, fairness, and system standardisation. Real‐world examples, such as Google Gboard and brain tumour segmentation, are presented to illustrate FL's practical impact. Finally, the paper discusses future directions, including potential integration with other AI techniques like reinforcement learning and transfer learning. This review provides valuable insights for researchers and professionals who are new to FL or seek a broader understanding of its ecosystem. While there are few studies that explore limited aspect of FL, this review adopts a holistic approach and covers all aspects of FL including foundational concepts, implementation, challenges faced by FL, and real‐world implementation. The broader scope, which spans FL from concepts to practical implementation, makes it particularly distinctive and a valuable contribution.

The rapid digitization of healthcare systems has led to a vast accumulation of electronic medical records (EMRs), offering an invaluable source of patient data that can significantly advance medical research and improve patient care. However, sharing EMRs for research purposes presents challenges, particularly concerning data privacy, security, and the limitations of traditional centralized data-sharing models. This paper introduces a novel approach that leverages blockchain technology to facilitate federated learning with EMRs, thereby addressing these challenges. Federated learning enables multiple institutions to collaboratively train a robust machine learning model without sharing raw data, preserving privacy and security. By integrating blockchain, this framework enhances data integrity, immutability, and trust, all in a decentralized environment. Blockchain serves as a transparent and secure ledger, recording model updates and aggregating them through a consensus-based mechanism. Smart contracts further enforce data usage policies, allowing only authorized access and maintaining control over data ownership and sharing. This approach empowers medical researchers and institutions to collaborate more effectively, accelerating the discovery of treatments, advancements in personalized medicine, and insights into rare diseases. It also enables patients to contribute to medical research while retaining control over their personal data, fostering a patient-centered approach to healthcare innovation. Experimental results confirm the efficacy and efficiency of this blockchain-enabled federated learning framework, highlighting its potential to transform medical research and adhere to stringent privacy and security standards. This study emphasizes the pivotal role of blockchain in enhancing Big Data analytics within healthcare, paving the way for improved collaboration, innovation, and patient outcomes.

Unmanned aerial vehicles (UAVs) have enabled numerous inventive solutions to multiple problems, considerably facilitating our daily lives; however, UAVs frequently rely on an open wireless channel for communication, making them susceptible to cyber-physical threats. Also, UAVs cannot execute complicated cryptographic algorithms due to their limited onboard computing capabilities. Balancing high-security levels and minimum computation costs is imperative when developing a security solution for UAVs. Consequently, several proxy signature schemes have been proposed in the literature to fulfill these requirements. Nevertheless, many of these solutions face the issue of high computation costs, and some exhibit security vulnerabilities that could not be more feasible options for UAV communication. Considering these constraints in mind, in this article, we introduce an improvised certificate-based proxy signature scheme (ICPS), which leverages the concept of hyperelliptic curve cryptography (HECC) to meet the security and efficiency requirements of UAV networks. The proposed ICPS scheme offers a range of notable features, including its ability to address key escrow and secret key distribution issues. The proposed ICPS scheme’s security hardness has been evaluated using the widely known security tool, the random oracle model (ROM), proving its resilience against known and unknown cybersecurity threats. Finally, this study conducts a performance comparison of the proposed scheme against existing schemes, emphasizing its outstanding cost-efficiency. Notably, the computation cost is measured at 5.3536 ms and the communication cost at 1120 bits, substantially lower than relevant existing schemes.

Unmanned aerial vehicles (UAVs) in beyond 5G (B5G) are crucial for revolutionizing various industries, including surveillance, agriculture, and logistics, by enabling high-speed data transfer, ultra-low latency communication, and ultra-reliable connectivity. However, integrating UAVs into B5G networks poses various security and privacy concerns. These risks encompass the possibility of unauthorized access, breaches of data, and cyber-physical attacks which jeopardize the integrity, confidentiality, and availability of UAV operations. Moreover, UAVs in B5G networks are also at high risk from the application of machine learning (ML)-based attacks by exploiting vulnerabilities in ML models, leading to adversarial manipulation, data poisoning and model evasion techniques, which can compromise the integrity of UAV operations, lead to navigation errors, and expose sensitive data collected by UAVs. Considering the aforementioned security and privacy concerns, this review article presents emerging security and privacy solutions for UAVs in B5G networks. Firstly, We introduce the essential background of integrating UAVs into B5G networks and discuss the advantages and security challenges which the emerging integrated network architecture have. Then, we proceed to analyze and examine the security and privacy landscape by including threats and requirements of UAVs in B5G networks. Based on these threats and requirements, solutions from physical layer security (PLS), blockchain (BC), federated learning (FL) and post-quantum cryptography (PQC) are discussed and explored in details. Moreover, potential future research directions are discussed in details as open research issues.

Most existing methods for detecting fraudulent news or other forms of disinformation primarily rely on user profiling or content analysis, determining whether a given article aligns with a user’s stylistic or content-related preferences. This research departs from conventional approaches by concentrating not only on the characteristics of shared content but also on user interactions and the resultant topologies of content propagation trees. In our study, we have introduced a deep learning model based on Graph Convolutional Neural Networks (GCNN) and enhanced with multi-head attention mechanisms. This model leverages a wide range of psycholinguistic attributes extracted from users’ posts, including sentiment, emotional content, linguistic features, personality traits, readability, and communication style. We further enrich these attributes with BERT embeddings to improve textual representation. Our research framework involves creating two distinct graph networks: the user interaction graph and the semantic propagation graph. These graphical representations are essential for visualizing dynamic interactions and patterns of information dissemination among users. For each user, we generate a set of carefully crafted features that capture their unique characteristics and behaviors. These user-specific features are then integrated into the User Interaction Graph, enabling a more nuanced understanding and representation of user interactions within our proposed model. The proposed framework has demonstrated superior performance on benchmark datasets, achieving accuracies and precisions of 91.07% and 91.24% on the FakeNewsNet dataset, and 94.20% and 94.92% on the FibVid dataset, respectively. The results obtained from the experimental evaluation have also revealed the effect of various parameters significant to profile disinformers, signifying a substantial improvement in the field of fake news detection and profiling.

The Sixth-Generation (6G) mobile network has the potential to provide not only traditional communication services but also additional processing, caching, sensing, and control capabilities to a massive number of Internet of Things (IoT) devices. Meanwhile, a 6G mobile network may provide global coverage and diverse quality-of-service provisioning to the Maritime Transportation System (MTS) when enabled through satellite systems. Although modern MTS has gained significant benefits from Internet of Things (IoT) and 6G technologies, threats and challenges in terms of security and privacy have also been grown substantially. Tracking the location of vessels, GPS spoofing, unauthorized access to data, and message tampering are some of the potential security and privacy vulnerabilities in the 6G-IoT enabled MTS. In this article, we propose a lightweight authentication protocol for a 6G-IoT enabled maritime transportation system to efficiently assist and ensure the security and privacy of maritime transportation systems. To validate the security characteristics, formal security assessment methods are utilized, i.e., Real-Or-Random (ROR) oracle model. The findings of the security analysis show that the proposed scheme is more secure than the existing schemes.

Fifth-generation (5G) cellular networks have led to the implementation of beyond 5G (B5G) networks, which are capable of incorporating autonomous services to swarm of unmanned aerial vehicles (UAVs). They provide capacity expansion strategies to address massive connectivity issues and guarantee ultra-high throughput and low latency, especially in extreme or emergency situations where network density, bandwidth, and traffic patterns fluctuate. On the one hand, 6G technology integrates AI/ML, IoT, and blockchain to establish ultra-reliable, intelligent, secure, and ubiquitous UAV networks. 6G networks, on the other hand, rely on new enabling technologies such as air interface and transmission technologies, as well as a unique network design, posing new challenges for the swarm of UAVs.Keeping these challenges in mind, this article focuses on the security and privacy, intelligence, and energy-efficiency issues faced by swarms of UAVs operating in 6G mobile network. In this state-of-the-art review, we integrated blockchain and AI/ML with UAV networks utilizing the 6G ecosystem. The key findings are then presented, and potential research challenges are identified. We conclude the review by shedding light on future research in this emerging field of research.

The Internet of Vehicles (IoV) is considered an enhancement of existing vehicular ad-hoc networks, which helps connect mobile vehicles to the Internet of Things (IoT) with the support of 5G networks. To assure the quality-of-service demand by the users, the edge computing paradigm of 5G networks can be incorporated in the IoV environment for supporting compute-intensive applications. The basic safety messages are typically transmitted using a multicast pattern in the IoV-enabled edge computing paradigm. The use of the multicast channel may accelerate the communication process; however, it is prone to various attacks due to the open nature of wireless networks. This article proposes a multimessage and multireceiver signcryption scheme for the multicast channel in a certificateless setting to solve the key escrow problem. The security of the partial private key is dependent on the secure channel, which increases the complexities of the system. Therefore, in the proposed scheme, we introduce a new idea that does not require a secure channel. The key generation center only sends the pseudo partial private key of the users on a public channel. Furthermore, the proposed scheme is based on hyper-elliptic curve cryptography (HECC), which has much smaller key sizes as compared to elliptic curve cryptography (ECC). The security proofs and performance comparison for our scheme are carried out. The findings show that the proposed scheme provides high security while using less computational and communication costs.

In this article, unmanned aerial vehicles (UAVs) are expected to play a key role in improving the safety and reliability of transportation systems, particularly where data traffic is nonhomogeneous and nonstationary. However, heterogeneous data sharing raises plenty of security and privacy concerns, which may keep UAVs out of future intelligent transportation systems (ITS). Some of the well-known security and privacy issues in the UAV-enabled ITS ecosystem include tracking UAVs and vehicle locations, unauthorized access to data, and message modification. Therefore, in this article, we contribute to the sum of knowledge by combining the hyperelliptic curve cryptography (HECC) techniques, digital signature, and hash function to present a privacy-preserving authentication scheme. The security features of the proposed scheme are assessed using formal security analysis methods, i.e., real-or- random (ROR) oracle model. To examine the performance of the proposed scheme, a comparison with other existing schemes has been carried out. The results reveal that the proposed scheme outperforms its counterpart schemes in terms of computation and communication costs.

A Smart Community (SC) is an essential part of the Internet of Energy (IoE), which helps to integrate Electric Vehicles (EVs) and distributed renewable energy sources in a smart grid. As a result of the potential privacy and security challenges in the distributed energy system, it is becoming a great problem to optimally schedule EVs’ charging with different energy consumption patterns and perform reliable energy trading in the SC. In this paper, a blockchain‐based privacy‐preserving energy trading system for 5G‐deployed SC is proposed. The proposed system is divided into two components: EVs and residential prosumers. In this system, a reputation‐based distributed matching algorithm for EVs and a Reward‐based Starvation Free Energy Allocation Policy (RSFEAP) for residential homes are presented. A short‐term load forecasting model for EVs’ charging using multiple linear regression is proposed to plan and manage the intermittent charging behavior of EVs. In the proposed system, identity‐based encryption and homomorphic encryption techniques are integrated to protect the privacy of transactions and users, respectively. The performance of the proposed system for EVs’ component is evaluated using convergence duration, forecasting accuracy, and executional and transactional costs as performance metrics. For the residential prosumers’ component, the performance is evaluated using reward index, type of transactions, energy contributed, average convergence time, and the number of iterations as performance metrics. The simulation results for EVs’ charging forecasting gives an accuracy of 99.25%. For the EVs matching algorithm, the proposed privacy‐preserving algorithm converges faster than the bichromatic mutual nearest neighbor algorithm. For RSFEAP, the number of iterations for 50 prosumers is 8, which is smaller than the benchmark. Its convergence duration is also 10 times less than the benchmark scheme. Moreover, security and privacy analyses are presented. Finally, we carry out security vulnerability analysis of smart contracts to ensure that the proposed smart contracts are secure and bug‐free against the common vulnerabilities’ attacks. The results show that the smart contracts are secure against both internal and external attacks.