ABSTRACT The proliferation of Open Radio Access Network (O‐RAN) architectures in 5G/6G networks introduces unprecedented cybersecurity challenges. Strategic base station deployment constitutes a fundamental determinant of network security posture and cyberattack resilience. In this paper, a novel cybersecurity‐driven deployment strategy for resilient base station positioning using an intelligent Resilient Ant Colony Optimization (iResACO) algorithm. The algorithm integrates security considerations directly into deployment optimization, employing bio‐inspired collective intelligence to discover patterns that balance coverage efficiency with attack resilience. Through extensive simulations in a 3.6 km 3.6 km urban environment in Riyadh, Saudi Arabia, experimental results demonstrate superior performance achieving 92.04% overall effectiveness with 96.0% coverage probability and 100% critical infrastructure protection. Under various cyberattack scenarios ranging from random to coordinated sophisticated attacks, the algorithm maintains coverage above 87% while preserving complete protection of critical facilities. The proposed approach provides a practical framework for deploying secure, resilient 5G/6G networks capable of withstanding evolving cyber threats while ensuring uninterrupted service to essential infrastructure.

In the era of digital intelligence, wearable devices act as pivotal tools for monitoring mental health data. These data facilitate the delivery of targeted support via mental health intervention platforms. As this integrated model gains traction, the selection of appropriate platforms has become essential to ensure effective mental health services for users. Given the involvement of large-scale users with conflicting opinions, this selection issue constitutes a large-scale group decision-making problem, underscoring the need for group consensus. Within this context, effectively addressing trust semantics and minority opinions emerges as two major challenges. To address these challenges, this article explores the cooperative mental health monitoring group consensus based on dynamic trust semantics and behavioral cultural modeling. First, to address trust propagation and evolution within dynamic trust semantics, an ordered trust propagation method and a trust evolution model are constructed to establish a complete and reliable social network among users. Second, a comprehensive index is designed based on opinion similarities and trust relationships to guide the Leiden community detection, thereby reducing the user dimensionality. Third, an objective minority opinions handling method is explored. Specifically, the user weight identified as having minority opinions is increased, while an adjustment strategy that considers the confidence level is applied to remaining users. Finally, the effectiveness and robustness of the proposed group consensus method for mental health monitoring are demonstrated via extensive experimental validation.

ABSTRACT Smart cities use information and communication technology to promote citizen welfare and economic growth within a sustainable environment. To guarantee that different urban actors, including people, devices, companies, and governments, can communicate efficiently, securely, and reliably, a robust, adaptable network infrastructure is required. However, the increasing complexity of the systems involved poses a challenge to smart city network modeling. Network topology generators produce synthetic networks that can reflect the underlying properties of real‐world networks, providing a practical approach to designing, testing, and implementing complex systems such as smart cities, yet the limited number of network topology generators for smart city applications has long prevented the proper development, investigation, and evaluation of various network configurations. In this article, a novel Smart City Network Topology Graph Generator (SCGG) is proposed to create a pseudorandom topology that mimics real smart city networks. The main goal of SCGG is to generate a network topology for smart cities that captures the interconnectivity of several communication technologies, such as wireless sensor networks (WSN), Internet of Things (IoT), and cellular networks. The SCGG system is characterized by the number of clusters, the average number of nodes, the number of layers, and the node density. The general network architecture and path‐related variables of the generated topologies are evaluated based on different graph theory measures, focusing on both global graph‐level characteristics and local node‐level features. The experimental results, demonstrating high natural connectivity and a low spectral radius value, offer a reliable tool for optimizing and strengthening the behavior and performance of smart city networks under different conditions to improve their robustness, minimize the probability of disruptions or failures, and enhance overall efficiency to ensure a resilient network.

ABSTRACT The rapid expansion of edge industrial Internet of things (Edge‐IIoT) has transformed industrial operations while introducing critical security challenges at the network edge. The growing sophistication of cyber attacks targeting Edge‐IIoT networks, particularly in resource‐constrained industrial environments, necessitates advanced detection mechanisms capable of identifying and classifying diverse attack patterns at the edge. This article presents a comprehensive edge‐centric attack detection framework leveraging pretrained deep learning models for securing Edge‐IIoT networks. Our methodology uses five state‐of‐the‐art pretrained models, GoogleNet, AlexNet, EfficientNetB0, ResNet50, and MobileNet, evaluated on the Edge‐IIoTset dataset comprising 2,219,201 network flow samples across 15 distinct attack classes. The framework efficiently processes many input features extracted from edge network traffic, including basic network characteristics, protocol headers, and industrial application‐level attributes specific to Edge‐IIoT environments. The experimental results demonstrate that GoogleNet achieves the highest accuracy of 97% and lowest performance degradation compared to other pretrained models with AlexNet at 96.85%, EfficientNetB0 at 96.81%, ResNet50 at 96.7%, and MobileNet at 96.42% in edge environments. Furthermore, our proposed approach significantly outperforms existing Edge‐IIoT security studies using the same dataset by up to 4.2%.

Facial forgery technology is advancing rapidly, leading to significant social security concerns. In recent years, as forgery technologies and types continue to emerge, many methods struggle to strike a balance between accuracy and robustness. Most existing methods rely on CNN to extract high-quality forged face clues but often overlook inherent forgery traces. Consequently, they may overfit the training dataset and perform poorly on data from diverse sources or be subjected to various post-processing operations. To address this challenge, we propose leveraging multi-scale texture information to expose subtle artifacts in RGB space. To achieve this, we devise a two-stream detection architecture that integrates texture features and RGB features. We analyze forgery traces using global large texture information and local detailed texture information separately. Additionally, we design a feature pyramid to fuse these two texture features and employ an attention mechanism to enhance the features of both streams. By examining forgery traces from multiple perspectives, we have developed an adaptive feature fusion module to facilitate interactive feature fusion between the two streams. We conduct extensive experiments on various benchmark datasets and compare our method with recent state-of-the-art (SOTA) methods to demonstrate its effectiveness. Our code will be provided at https://github.com/hryyyy/MST .

ABSTRACT Deepfake, as a popular form of visual forgery technique on the Internet, poses a serious threat to individuals' data privacy and security. In consumer electronics, fraudulent schemes leveraging Deepfake technology are widespread, making it urgent to safeguard users' data privacy and security. However, many Deepfake detection methods based on Convolutional Neural Networks (CNNs) struggle to achieve satisfactory performance on mainstream datasets, especially with heavily compressed images. Observing that tampered images leave traces in the frequency domain, which are imperceptible to the naked eye but detectable through spectrum analysis, this study proposes a novel face forgery detection framework integrating spatial and frequency domain features. The framework introduces three innovative modules: the cross‐attention fusion module (CAFM), the guided attention module (GAM), and the multi‐scale feature fusion module (MSFFM), Specifically, CAFM combines spatial and frequency‐domain features through cross‐attention to enhance feature interaction. GAM generates attention maps to refine the integration of spatial and frequency features, while MSFFM fuses multi‐scale hierarchical features to capture both global and local tampering artifacts. These modules collectively improve the richness and discrimination of the extracted features, contributing to the overall detection performance. The proposed method demonstrates its effectiveness and superiority in forgery detection tasks, achieving a 3.9% average improvement in AUC compared to the state‐of‐the‐art method GocNet [1] on FaceForensics++ (FF++) and WildDeepfake datasets. Extensive experiments further validate the effectiveness of our approach.

Maritime communication, critical for global oceanic trade, faces challenges and opportunities with advancements in Information and Communication Technology (ICT). Traditional methods are susceptible to interception due to open channels, limited authentication, jamming, and other security risks. Securing vessel movements and locations in Internet of Vessels (IoV) is essential to prevent unauthorized data interception and tampering during transmission. We propose a ship authentication method using location-based secure keys to ensure the confidentiality of a vessel’s whereabouts. The proposed scheme’s robustness is validated through formal and informal security analyses, and formal verification using the Scyther automated verification tool, demonstrating its effectiveness against potential attacks in maritime networks. Comparative studies indicate that utilizing location-based keys maintains anonymity and untraceability without imposing significant computational or communication burdens. Experimental findings from comprehensive big data analytics and simulations using NS3 validate the scheme’s feasibility and performance.

Intelligent supply line surveillance is critical for modern smart grids (SGs). Smart sensors and gateway nodes are strategically deployed along supply lines to achieve intelligent surveillance. They collect data continuously and transmit it to the control centre in real-time. It enables real-time monitoring, fault detection, and efficient energy management across distribution networks. This advanced surveillance system ensures continuous monitoring of supply lines, detecting anomalies, and optimizing operations to maintain the stability and reliability of the SG. However, the reliance of all participating nodes on public communication channels to transmit supply line surveillance data exposes these systems to critical cyber attacks. These cyber attacks include impersonation, physical tampering, ephemeral secret leakage (ESL), and desynchronization attacks. To address these issues, existing key-exchange protocols often fail to ensure robust security while imposing high computation and communication overheads, limiting their practicality for resource-constrained environments. Therefore, we propose a secure and efficient key exchange and tamper-resistant authentication protocol using elliptic curve cryptography (ECC) and physical unclonable functions (PUFs). The PUF mechanism provides robust resistance against physical tampering and cloning attacks, ensuring enhanced physical security for supply line devices. We validate the security robustness of the devised protocol using the random or real (ROR) model. Furthermore, informal security analysis demonstrates the protocol’s robustness in rigorously resisting various attacks, including physical tampering, impersonation, ESL and desynchronization. Moreover, we determine the performance evaluation that reveals the protocol’s superior efficiency compared to competing protocols, achieving significant reductions of 28.64% in computation overhead and 9.96% in communication overhead.

Phishing attacks pose significant cybersecurity threats in the metaverse, particularly in virtual gaming environments where sensitive identity information and digital assets are at risk. This paper introduces a blockchain-based anti-phishing authentication protocol that enhances the security and efficiency of virtual game recharge orders. The proposed protocol integrates elliptic curve cryptography, symmetric encryption, and Chebyshev chaotic mapping to establish a robust three-factor authentication mechanism. By leveraging blockchain’s decentralized and tamper-proof properties, the protocol securely stores identity information and authentication keys, enabling users to effectively identify phishing websites and ensure secure cross-platform transactions. Security analysis using the Real-Or-Random (ROR) model demonstrates the protocol’s resistance to phishing attacks, and its provision of forward security and anonymity. Comparative performance evaluation also highlights the protocol’s significant advantages in computational and communication efficiency over existing methods. This study contributes to the advancement of secure virtual transactions, fostering trust and sustainability in the metaverse economy.

The Internet of Vehicles (IoV), equipped with sensors, generates vast amounts of data, demanding rigorous computation and network. The cloud computing (CC) platform meets these stringent computation requirements, but it has a significant latency that fog computing (FC) effectively handles. Software defined network (SDN) has become the de facto standard for next-generation networking due to its unique and flexible features, which handle the network prerequisites in an agile manner. Task offloading (TO) is a crucial issue in software defined-IoV (SD-IoV), particularly when the vehicle’s resources are insufficient. Fog nodes schedule offloaded tasks; however, neglecting computational and network details during task scheduling can lead to longer completion times, thereby restricting the functionality of SD-IoV due to its time-sensitive nature. Therefore, this article introduces two proactive intelligent TO (ITO’ and ITO) schemes. These schemes schedule tasks onto fog nodes with enhanced computational capabilities, dynamically enable the network through the ovs-ofctl SDN utility, and consider vehicle mobility during task scheduling. Experiments with Mininet show that the proposed (ITO’ and ITO) schemes improve performance in terms of CPU availability, bandwidth, and throughput by 5.2 times, 50% and (30.7–13.9)% more than the existing scheme. They also reduce average packet loss, delay, round trip time (RTT), and offloaded node time selection by (45.35–21.3)%, (50–33.3)%, (53.9–9.9)% and (43.2–26.06)% which demonstrates the efficacy of the proposed scheme.

Wireless sensor networks (WSNs), pivotal in the industrial Internet of Things (IIoT), encompass resource-limited sensor nodes, users, and gateways. Advancements in Internet technologies have substantially facilitated remote data access, rendering WSNs indispensable across various sectors, such as defense, agriculture, disaster management, and healthcare, where they serve as pivotal components for remote monitoring and control mechanisms. Within the IIoT framework, the transmission of critical and sensitive information over public channels presents significant security challenges. Such challenges disrupt operations and compromise the integrity and reliability of industrial processes. The system must include an authentication mechanism to tackle this critical issue that resists potential security threats. Consequently, this article introduced a reliable and secure the three-factor authentication protocol tailored for IIoT environments. The proposed protocol aims to mitigate unauthorized access and safeguard the integrity of industrial operations. We comprehensively evaluated the protocol’s robustness and security efficiency by employing informal and formal security analysis techniques, highlighting its effectiveness in resisting potential threats. This proposed protocol fortifies the network against potential security threats, ensuring security and system reliability in industrial applications. This protocol assists only legitimate users in accessing the sensing devices remotely. Moreover, the statistical results endorse the resource efficiency of the devised protocol as it achieves 43.2% and 35.8% efficiency in terms of communication and computational costs, respectively.

The Internet of Drones (IoD) offers supervised admittance to drones in a targeted fly zone as the byproduct of the Internet of Things (IoT). The term drone is the trendy alias for intelligent flying vehicle (IFV). The contemporary sensing, processing, and connectivity services enrich the use of drones in many civilian and military applications. In these applications, consumers can acquire real-time information directly from flying drones in a smart city environment. While this feature undeniably empowers consumers, it poses significant security risks due to the direct access privilege. We propose an anonymous protocol for consumer flying vehicles within smart city applications to mitigate these threats. The proposed protocol utilizes a physically unclonable function to sustain the physical security of flying vehicles. We ratify our protocol’s security fortitude and persistence through inclusive security analysis. We demonstrate the performance evaluation under diverse performance metrics, which shows that the proposed protocol achieves 40.69% and 17.91% efficiency as compared to related protocols in terms of computation and communication cost comparison, respectively.

In the evolving landscape of supply chain management, the integration of radio-frequency identification (RFID) technology has marked a significant milestone. This development has led to the emergence of a new system in RFID-based supply chain management, which is intricately linked with the advances in the Internet of Things (IoT). RFID technology employs electromagnetic fields to identify and track tags on objects and revolutionizes the management and tracking of items in the supply chain. However, the public communication among RFID tags, RFID readers, and supply chain infrastructure predominantly escalates security and privacy challenges. Several authentication protocols have been proposed to overcome these challenges. However, the vulnerability of most proposed protocols to numerous security attacks renders them inefficient. Therefore, to address these crucial challenges, we devised an RFID-based authentication protocol for supply chain management systems. The incorporation of a physically unclonable function (PUF) into the protocol fortifies the system against physical tampering attacks. To validate the security and effectiveness of the devised protocol, both informal and formal security analysis are conducted. The formal security analysis is conducted using the widely used random oracle model. The informal security analysis reveals that the devised protocol provides enhanced and efficient security features. Furthermore, we perform a comparative analysis with related protocols, focusing on critical performance metrics like communication cost, computation cost, and overall security features. The results of the comparative analysis are promising, indicating a substantial 30.92% reduction in computational cost and a 23.98% reduction in communication cost in comparison to related protocols, thus highlighting the protocol’s superior performance and resource efficiency.

Digital transformation is changing the entire landscape of the financial industry. The increasing customer demand to address, or at least have a positive impact on social problems, fuel the rapid growth of social fintech companies. However, as these companies scale significantly, they face critical challenges in managing employee stress, which can lead to decreased performance and high turnover rates. Following the rise of ChatGPT, large language models (LLMs) have been increasingly utilized in mental health-related applications and offering a promising solution for social fintech companies to support their employee’s mental health. Nevertheless, existing LLMs are predominantly English-focused, limiting their effectiveness in addressing mental health support across diverse linguistic groups. To address this gap, we propose a novel multilingual adaptation of widely used mental health datasets, translated from English into the two most widely spoken languages globally—Mandarin and Spanish. This adaptation enables a comprehensive evaluation of LLMs, such as GPT and Llama, in detecting and assessing mental health conditions across different languages. Initially, we used ChatGPT-4o-Mini to translate the original English dataset into Spanish and Mandarin. We then evaluate the performance of these translated datasets using various state-of-the-art LLMs. Additionally, we analyze the relationship between sentence length and prediction performance. Our experiments reveal significant variability in model performance, with language-specific nuances and disparities in mental health data coverage posing challenges to achieving consistent accuracy.

The rapid advancement of large language models (LLMs) has opened up new possibilities for transforming healthcare practices, patient interactions, and medical report generation. This paper explores the application of LLMs in developing medical chatbots and virtual assistants that prioritize clinical accuracy. We propose a novel multi-turn dialogue model, including adjusting the position of layer normalization to improve training stability and convergence, employing a contextual sliding window reply prediction task to capture fine-grained local context, and developing a local critical information distillation mechanism to extract and emphasize the most relevant information. These components are integrated into a multi-turn dialogue model that generates coherent and clinically accurate responses. Experiments on the MIMIC-III and n2c2 datasets demonstrate the superiority of the proposed model over state-of-the-art baselines, achieving significant improvements in perplexity, BLEU-2, recall at K scores, medical entity recognition, and response coherence. The proposed model represents a significant step in developing reliable and contextually relevant multi-turn medical dialogue systems that can assist patients and healthcare professionals.

Vehicular crowdsensing (VCS) has aroused extensive attention because of its ability to provide comprehensive data services for intelligent transportation systems. Wherein, secure data transmission is a prerequisite for realizing the above benefits of VCS. Unfortunately, although many works on secure data sharing have been proposed, these schemes suffer from practical weaknesses such as data source authentication, malicious identity traceability, and inefficiency. In this paper, we propose a practical conditional anonymization and leakage-resilient authentication solution for vehicular crowdsensing communication (CALRA). Our proposal not only resists the leakage of sensitive information about vehicles but also realizes the authentication of data senders, while guaranteeing the integrity, authenticity, and confidentiality of data. Besides, CALRA exploits traceability technology to pursue malicious/illegal participants, thus avoiding participants’ accountability evasion caused by absolute anonymity. Furthermore, our CALRA solution delegates complex computational processes into an offline formulation to reduce computational and communication overheads. Finally, our scheme is proved to be secure and unforgeable through the random oracle model, and the performance evaluation illustrates that our CALRA proposal is superior and practical.

Internet of Things (IoT) devices are crucial components in e-healthcare networks. It enables remote patient health monitoring and facilitates seamless communication among medical sensors, wearable devices, and healthcare providers through public communication channels. Despite these advantages, the use of public communication among medical sensors in e-healthcare networks introduces critical challenges, such as vulnerability to impersonation, physical capture, and ephemeral secret leakage, particularly in resource-constrained environments. In recent years, various access control protocols have been developed to mitigate these risks. However, these protocols often fail to ensure robust security while incurring significant communication and computation overhead. To overcome these limitations, we propose a lightweight and robust access control protocol for IoT-based e-healthcare networks using chaotic maps. We propose a novel protocol that integrates a PUF-based mechanism to mitigate the challenges of physical tampering and cloning attacks in e-healthcare networks. It leverages the inherent uniqueness of PUF and enhances security through the high-entropy properties of chaotic maps. We analyze the proposed protocol informally, which confirms that it significantly bolsters efficiency and security. We also validate the security using the Random or Real (RoR) model. Moreover, we verify the security of the proposed protocol using Scyther. These analyses highlight that the proposed protocol offers robust resistance to numerous attacks, such as impersonation, physical capture, and ephemeral secret leakage. Moreover, we also compare it with existing and relevant protocols. The comparative analysis showcases its superior performance. Notably, the proposed authentication protocol significantly reduces 46.84% computational overhead and decreases 31.30% communication overhead, underscoring its enhanced performance and resource efficiency.

For Wireless Body Area Networks (WBANs), the security of sensitive data of patients is of the utmost importance, particularly in healthcare environments. This study presents a novel methodology for improving the efficacy of signature aggregation in a scenario involving doctors and patients while mitigating concerns about location privacy. Though there have been prior proposals for signature aggregation schemes, the proposed approach seeks to optimize the aggregation process within the considered scenario, thereby improving performance and reducing computational and communication burden. In addition, the proposed scheme integrates a resilient mechanism that safeguards the doctor’s location privacy by utilizing the Chinese Remainder Theorem (CRT). Advanced cryptographic algorithms and location-anonymization techniques are employed in the proposed method to safeguard the confidentiality of the doctors’ location. The security of the proposed scheme is formally analyzed using the Burrows-Abadi-Needham (BAN) logic and formally verified using the automated software validation tool, known as the Scyther tool, and an informal analysis of various security attributes confirms the security robustness of the proposed scheme. The efficacy is evaluated in comparison to analogous works utilizing the Cygwin software. The performance evaluation shows that the proposed scheme has lower communication costs as compared to existing competing schemes. Moreover, the serving ratio in the proposed scheme is high even if the number of patients is low for doctors.

Summary Software‐defined network (SDN) technology is widely used for computer networks, especially in enterprise data centers and virtualized networking. However, SDN networks encounter severe challenges to security. One such challenge comes from third‐party applications that contain malicious logic and security vulnerabilities, resulting in controller integrity attacks. In this paper, we propose a defensive mechanism of object integrity for SDN (DMoiSDN) to mitigate the issue known as Cross‐App Poisoning (CAP). Our results contribute to increasing the integrity level of the controller's resources by conducting a potential risk analysis, which showed a decrease of 57% in the risk factor for potential attacks. We further examined the results of comparing DMoiSDN's performance with related work that uses information flow control (IFC) policies. The best results among the three conducted scenarios were as follows: we found that decreased latency in our system ranged from 12% to 90%, with an average of 59%, when encountering an increase in requests. It ranged from 78% to 49%, with an average of 63%, when receiving a variable number of total permissions for each application. DMoiSDN is expected to show a perceptible but reasonable latency and, to some extent, be able to avoid a critical impact on performance.

Abstract Smart city networks are critical for delivering essential services such as healthcare, education, and business operations. However, these networks are highly susceptible to a range of threats, including natural disasters and intentional cyberattacks, which can severely disrupt their functionality. To address these vulnerabilities, we present the resilient smart city (ResiSC) system, designed to enhance the resilience of smart city communication networks through a topological design approach. Our system employs a graph‐theoretic algorithm to determine the optimal network topology for a given set of nodes, aiming to maximize connectivity while minimizing link provisioning costs. We introduce two novel connectivity measurements, All Nodes Reachability (ANR) and Sum of All Nodes Reachability (SANR), to evaluate network resilience. We applied our approach to data from two public universities of different sizes, simulating various attack scenarios to assess the robustness of the resulting network topologies. Evaluation results indicate that our solution improves network resilience against targeted attacks by 38% compared to baseline methods such as k‐nearest neighbours (k‐NN) graphs, while also reducing the number of additional links and their associated costs. Results also indicate that our proposed solution outperforms baseline methods like k‐NN in terms of network resilience against targeted attacks by 41%. This work provides a practical framework for developing robust smart city networks capable of withstanding diverse threats.

Abstract Smart device manufacturers rely on insights from smart home (SH) data to update their devices, and similarly, service providers use it for predictive maintenance. In terms of data security and privacy, combining distributed federated learning (FL) with blockchain technology is being considered to prevent single point failure and model poising attacks. However, adding blockchain to a FL environment can worsen blockchain's scaling issues and create regular service interruptions at SH. This article presents a scalable Blockchain‐based Privacy‐preserving Federated Learning (BPFL) architecture for an SH ecosystem that integrates blockchain and FL. BPFL can automate SHs' services and distribute machine learning (ML) operations to update IoT manufacturer models and scale service provider services. The architecture uses a local peer as a gateway to connect SHs to the blockchain network and safeguard user data, transactions, and ML operations. Blockchain facilitates ecosystem access management and learning. The Stanford Cars and an IoT dataset have been used as test bed experiments, taking into account the nature of data (i.e. images and numeric). The experiments show that ledger optimisation can boost scalability by 40–60% in BCN by reducing transaction overhead by 60%. Simultaneously, it increases learning capacity by 10% compared to baseline FL techniques.

Industrial control systems (ICSs) are crucial in managing critical infrastructure, making their security a paramount concern. In recent years, their widespread adoption, together with the overall distance spanned by the critical infrastructure of industrial communication networks, have increased the complexity of the networks’ topological arrangement, increasing their structural vulnerabilities. In this scenario, deep learning models, especially those that incorporate graph-aware mechanisms, have arisen as a promising solution. This paper presents a novel centrality-and graph-aware attack detector (CGAAD) that includes nodes’ significance by centrality measures within a graph convolution network (GCN) framework to provide superior cyberattack detection performance and increase the resilience of critical ICS infrastructure. The proposed CGAAD model is in three parts. First, centrality measures are used as features for each of the nodes in the ICS graph topology. Then, a sparse-autoencoder (sparse-AE) enhances the feature representations to harness the subsequent classification step. Finally, the GCN leverages the graph structure and the enhanced features to classify dataflow between nodes as either normal or attacked. Experimental results demonstrate promising performance, reaching nearly 99% in terms of accuracy and F1-score, reducing misclassifications of both normal and attacked samples, which is crucial in ICS critical infrastructure applications.

The Internet of Things (IoT) is a network of interconnected objects, which congregate and exchange gigantic amounts of data. Usually, pre-deployed embedded sensors sense this massive data. Soon, several applications of IoT are anticipated to exploit emerging 6G technology. Healthcare is one of them, where the 6G-inspired paradigm may facilitate the users to exchange information through hundreds of sensors under the assumption of Artificial Intelligence of Things (AIoT). Integration of medical sensors with AIoT is known as Artificial Intelligence of Medical Things (AIoMT). The secure and seamless interactions among 6G-enabled AIoMT users should be the primary challenge. Furthermore, resource-constrained wearable sensing devices, with their inability to execute complex security solutions, provide an ideal attraction for malicious entities to launch diverse attacks. These challenges have motivated us to design a cost-effective authenticated solution (CAS) for 6G-enabled AIoMT healthcare applications. Our CAS protocol not only prevents cyber threats like impersonation session key secrecy, but it can also prevent physical threats like hardware tampering. We observe formal and informal security validations to endorse its robustness and effectiveness. Performance comparison reveals that CAS protocol offers maximum security enrichment. Moreover, CAS is cost-effective as it has achieved 33% and 60% reduction in computation and communication overheads, respectively, compared to contemporary competing related protocols.

The increase in popularity of vehicles encourages the development of smart cities. With this advancement, vehicular ad-hoc networks, or VANETs, are now frequently utilized for inter-vehicular communication to gather data regarding traffic congestion, vehicle location, speed, and road conditions. Such a public network is open to various security risks. Overall, protecting personal information on VANET is a vital responsibility. The integration of fog computing and VANETs has gained significant importance in recent years, driven by advancements in cloud computing, Internet of Things (IoT) technologies, and intelligent transportation systems. However, ensuring secure communication in fog-based VANETs remains a major challenge. To overcome this challenge, we introduce a novel authenticated key agreement protocol that achieves mutual authentication, generates a secure session key for secret communication, and provides privacy protection without the use of bilinear pairing. We rigorously prove the security of our proposed protocol, which is designed specifically for fog-based VANETs, and has been shown to meet their stringent security requirements. Moreover, we performed formal and informal analysis that shows our proposed protocol is highly efficient,our protocol’s computational and communication overhead are lower than those of other relevant protocols by 45.570% and 29.432%, respectively. Finally we use NS-3 simulation to prove that our proposed algorithm is a practical and scalable solution for secure communication in fog-based VANETs.

The rise of smart cities is directly connected to the increasing use of vehicles. The growing vehicle utilization has driven the emergence of Vehicular Ad-hoc Networks (VANETs), facilitating instant information exchange among vehicles. The system provides essential information regarding road conditions, traffic patterns, and more relevant data. VANETs encompass two fundamental categories of communication exchanges, namely Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I). V2I technology facilitates the integration of cars and transportation infrastructure, enabling effective communication between vehicles and infrastructure. Nevertheless, the potential of V2I communication has various security concerns arising from prevalent security threats. Current authentication techniques encounter challenges regarding complexity, security, and privacy considerations. We designed a hash-based lightweight and anonymous authentication scheme to address the aforementioned restrictions and enhance the effectiveness of authentication in V2I architecture. This scheme effectively combines identity, password, and bio-metric to enhance resistance against impersonation, denial of service, and privileged insider attacks. The devised scheme distinguishes itself by a comparative analysis and security proofs, highlighting its superior capability in guaranteeing secure authentication in V2I communication. The comprehensive security analysis conducted formally and informally showcases the robustness of the proposed solution against several threats. The performance evaluation results show that our scheme demonstrates a decrease in the computational cost of 51.40% approximately and a reduction in communication overhead of around 22.57%. These results establish the efficiency and scalability of the proposed scheme as a viable solution for V2I architecture.

Vehicular network technology has made substantial advancements in recent years in the field of Intelligent Transportation Systems. Vehicular Cloud Computing (VCC) has emerged as a novel paradigm with a substantial increase in data exchange within Vehicular ad-hoc networks (VANETs). VCC integrates cloud computing, vehicular networking, and Internet of Things (IoT) technologies. It enables Infrastructure-to-Vehicle (I2V), Vehicle-to-Vehicle (V2V), and Vehicle-to-Device (V2D) communication. VCC optimizes vehicle, cloud infrastructure, and IoT resources while addressing significant communication security and vehicle-user privacy challenges. To address these issues, we developed an RFID-based authentication protocol for VCC based on a Henon map using a hash function. In addition, we also incorporated a Physical Unclonable Function (PUF) to resist physical tampering attacks. We validate the protocol’s security formally and informally. The formal security analysis is conducted through a widely used RoR model. We use the Scyther simulation tool to verify the proposed protocol’s security against various attacks. Moreover, we compare the performance of our protocol with similar existing protocols across important performance parameters such as communication and computation overheads and security attributes. The proposed protocol yields substantial improvements, demonstrating a 40.74% reduction in computation overhead and a 13.03% decrease in communication overhead as compared to related protocols, delivering both enhanced performance and resource efficiency. The analysis demonstrates its capacity to support secure communication in the VCC environment and satisfy desirable security attributes.

Summary In recent years, artificial intelligence techniques, such as software‐defined networks (SDNs), machine learning classification (ML classification), and mobility models (MMs), have become vital in developing networks. Furthermore, communication methodologies, such as handover, directly affect network performance. In this paper, we propose a new system named SSHS, SDN Seamless Handover System, that combines SDN with an ML classifier to administer the network connection of mobile nodes. Through the SSHS system, the SDN will centralize the control to enable comprehensive management over the network, coupled with a decision tree (DT) classifier in the RYU controller to bring intelligence to the SDN application by enabling data analysis and prediction among mobile nodes generated by the RSSGM model. We present the SSHS model's effectiveness in providing a seamless communication handover among multiple access points (APs). The results of this study revealed that the SSHS provided a seamless handover among APs by improving the throughput by 26%, and decreasing the delay of arriving packets by 73% to standard SDN handover system.

Summary Developing a long‐lasting, secure Industry 4.0 system presents a significant challenge for businesses and other interested parties. Industrial control systems (ICSs) are particularly vulnerable to cybercrime because of the operating systems' excessive availability and high robustness requirements. This research investigates five graph‐theory‐based measures to evaluate the robustness of industrial network topologies against three centrality‐based attacks and one random attack. Experiments are conducted to examine the three levels of the ICS network topology, from the field devices to the controllers and the enterprise devices. The results are twofold. On the one hand, the closeness‐based attack is the most harmful since it has the highest destructive potential and needs to attack only half of the total nodes in the network to reach the lowest robustness level. The betweenness‐based attack follows closely in terms of destruction, whereas the degree‐based attack is less destructive but rapidly degrades the robustness of the network. On the other hand, the flow robustness measure provides the best performance in the presence of any of the studied attacks, showing strong perception of robustness reduction when only one percent of the total nodes in the network are attacked. For this reason, the flow robustness measure is suitable to identify and locate the targeted attacks at their early stages, preventing them from becoming more catastrophic. Finally, the results suggest that the industrial network security system should combine at least two measures to ensure robustness against the most destructive attacks and their early‐stage detection. The research also confirmed the results by implementing attacks and measures on a real gas transmission network.

Summary Software‐defined networking (SDN) has been developed to separate network control plane from forwarding plane which can decrease operational costs and the time it takes to deploy new services compared to traditional networks. Despite these advantages, this technology brings threats and vulnerabilities. Consequently, developing high‐performance real‐time intrusion detection systems (IDSs) to classify malicious activities is a vital part of SDN architecture. This article introduces two created datasets generated from SDN using Mininet and Ryu controller with different feature extraction tools that contain normal traffic and different types of attacks (Fin flood, UDP flood, ICMP flood, OS probe scan, port probe scan, TCP bandwidth flood, and TCP syn flood) that is used for training a number of supervised binary classification machine learning algorithms such as k‐nearest neighbor, AdaBoost, decision tree (DT), random forest, naive Bayes, multilayer perceptron, support vector machine, and XGBoost. The DT algorithm has achieved high scores to fit a real‐time application achieving F1 score on attack class of 0.9995, F1 score on normal class of 0.9983, and throughput score of 6,737,147.275 samples per second with a total number of three features. In addition, using data preprocessing to reduce the model complexity, thereby increasing the overall throughput to fit a real‐time system.

Internet of Things (IoT) is an advanced technology that will have a significant impact on our day-to-day lives. Wireless sensor networks (WSN) form a basic element used within the IoT platforms. In WSNs, sensor deployment is a challenging task that needs to be addressed. Some applications of WSN require transmitting sensed information with low end-to-end delay. In addition, resilience against network attacks is an important issue that should be considered in the design of a WSN. In this paper, five static sensor deployment strategies for WSNs are investigated. The deployment strategies are a uniform random deployment and four regular deployments: square grid, triangle grid, hexagon grid, and tri-hexagon tiling (THT). The deployment schemes are evaluated under three performance metrics: area coverage, end-to-end delay, and resilience to attacks. In this work, the existence of obstacles in a sensing region is considered. Experimental results demonstrate that square grid deployment strategy exhibits superior performance compared to other strategies in terms of area coverage. In contrast, THT outperforms other strategies in terms of end-to-end delay. The regular deployment methods have higher resilience to attacks compared to random deployment methods.

We propose a cross-layer routing framework in the SDN (software-defined networking) domain to cope with regionally-correlated challenges. By taking advantage of the failure detection model, GeoDivRP calculates multiple geodiverse paths for resilient network communications. Coupled with the optimization model, it realizes the minimized delay-skew product when decoupling traffic onto multiple paths. We evaluate our framework using MPTCP (Multipath TCP) in the face of regionally-correlated failures and it presents better performance compared to the single path routing. We further demonstrate our web framework to automate the OpenFlow experiment by programmatically importing network topologies and execute challenge emulations using the user-provided challenge regions.

End-to-end communication in highly-dynamic airborne networks is challenging due to the presence of highly mobile nodes and the inherent nature of wireless communication channels. Domain-specific protocols are required that can address these challenges and enable reliable transmission of data in this environment. We develop the ANTP (airborne network and transport protocols) suite that operates in this highly-dynamic environment while utilising cross-layer optimisations between the physical, MAC, network, and transport layers. We show how each component in the ANTP suite outperforms the traditional TCP/IP and MANET protocols through simulation using ns-3. Having verified these protocols through simulation and analysis, the next step towards deployment of the ANTP suite is developing a cross-platform implementation of the protocols. Towards this end we present an architecture for the protocol stack to be implemented in the Python programming language.