The Internet of Medical Things (IoMT) introduces complex security challenges as interconnected medical devices enlarge the attack surface and limit the effectiveness of traditional intrusion detection systems (IDS). In this paper, we propose a Neuro-Symbolic Intent-Based Intrusion Detection System (NS-IBN) that integrates deep learning–based pattern recognition with symbolic reasoning to produce interpretable, intent-aligned security decisions. NS-IBN comprises an Intent-to-Symbol Translation Layer, an Intent-Driven Attention Mechanism, a Neural-Symbolic Synchronization Module, and a Symbolic Reasoning Engine that together link administrator-defined security intents to concrete detection behavior. In a representative intensive care unit (ICU) scenario with networked infusion pumps and vital-sign monitors, NS-IBN can be configured to detect lateral movement and unauthorized command injection while limiting disruptive false alarms for clinicians. Evaluation on the IoT-IDS2021 benchmark shows that NS-IBN achieves 98.3% accuracy, an explainability score of 0.94, and a 1.2% false positive rate, providing transparent and auditable intrusion detection for IoMT environments.

Integrating natural language processing (NLP) with generative pre-trained transformer (GPT) models plays a pivotal role in enhancing the accuracy and efficiency of healthcare software, which is essential for patient safety and providing high-quality care. The precision of healthcare software is fundamental to protecting the patient's well-being. In addition, it can ensure the delivery of superior care, maintain the integrity of healthcare systems, and promote trust and cost-effectiveness. It is necessary to emphasize the importance of software reliability in its development and deployment. Symbolic execution serves as a vital technology in automated vulnerability detection. However, it often faces problems such as path explosion, which seriously affects efficiency. Although several studies have been conducted to reduce the number of computational paths, this problem remains a significant obstacle. Therefore, more efficient solutions are urgently needed to ensure software security. This paper proposes a large-scale language model (LLM) induction method mitigating path explosion applied to symbolic execution engines. In contrast to traditional symbolic execution engines, which often result in timeout or out-of-memory detection, our approach achieves the task of detecting vulnerabilities in seconds. Furthermore, our proposal improves the scalability of symbolic execution, allowing more extensive and complex programs to be analyzed without significant increases in computational resources or time. This scalability is crucial to tackling modern software systems and improving the efficiency and effectiveness of automated defect verification in healthcare software.

The exponential growth of sensitive patient information and diagnostic records in digital healthcare systems has increased the complexity of data protection, while frequent medical data breaches severely compromise system security and reliability. Existing privacy protection techniques often lack robustness and real-time capabilities in high-noise, high-packet-loss, and dynamic network environments, limiting their effectiveness in detecting healthcare data leaks. To address these challenges, we propose a Swarm Intelligence-Based Network Watermarking (SIBW) method for real-time privacy data leakage detection in digital healthcare systems. SIBW integrates fountain codes with outer error correction codes and employs a Multi-Phase Synergistic Swarm Optimization Algorithm (MPSSOA) to dynamically optimize encoding parameters, significantly enhancing the robustness and interference resistance of watermark detection. Additionally, a reliable synchronization sequence and lightweight embedding mechanism are designed to ensure adaptability to complex, dynamic networks. Experimental results demonstrate that SIBW achieves over 90% detection accuracy under high latency jitter and packet loss conditions, surpassing existing methods in both robustness and efficiency. With a compact design of only 3.7 MB, SIBW is particularly suited for rapid deployment in resource-constrained digital healthcare systems.

Sequential recommendation is becoming more critical in a variety of e-commerce platforms. The aim of sequential recommender systems is to model the dynamic preferences of users based on their previous actions and predict what they will do next. The collected user activity logs on real-world platforms could be quite long. This wealth of information provides options to follow users’ actual interests. Prior efforts primarily aimed at providing recommendations following recent behaviors. Meanwhile, the entire sequential data may not be used efficiently since early actions may influence users’ decisions at present. Furthermore, scanning the whole behavior sequence while doing inference for every user is unbearable due to the need for prompt reaction time in real-world applications. To this end, we propose the DELIGHT Sequential Recommender System (DSRS), which takes the above properties into account to recommend the next item the user might be interested in. DSRS divides the entire user behavior sequence into long- and short-term segments and models them through independent networks before integrating their learned representations. In particular, the first network learns user long-term, whereas the second one learns short-term preferences and then combines them for an efficient joint recommendation. Experimental findings across four datasets show that our model outperforms other state-of-the-art sequential models in apprehending long-term dependence.

Web 3.0 is a grand design with intricate data interchange, implying the requirement of versatile network protocol to ensure its security. Attribute-based signature (ABS) allows a user, who is featured with a set of attributes, to sign messages under a predicate. The validity of the ABS signature demonstrates that this signature is generated by the user whose attributes satisfy the corresponding predicate, and thus flexibly achieves anonymous authentication. Similar to other digital signatures, the security of ABS is broken in case the private key of the user is leaked out. To address the threat brought by the key leakage, this paper proposes a puncturable attribute-based signature scheme that allows the private key generator to revoke the signing right associated with specific tags. This paper firstly elaborates the construction of the proposed ABS scheme with puncturable property, and then proves its security theoretically by reducing the involved security to the computational Diffie–Hellman assumption. This paper then experimentally shows that the suggested puncturable ABS scheme owns a more efficient storage cost and superior performance.

The Internet of Medical Things (IoMT) has transformed healthcare by integrating medical devices, wearables, and electronic medical records (EMRs). These technologies enable real-time diagnostics, personalized monitoring, and proactive healthcare delivery. However, applying the IoMT to critical areas like lung cancer detection remains challenging due to multimodal data heterogeneity, real-time processing requirements, and resource constraints nature of the devices. To address these challenges, in this paper, we present a novel IoMT-based framework for lung cancer diagnostics that unifies imaging data, sensor readings, and EMRs into a cohesive pipeline. The proposed framework employs convolutional neural networks, vision transformers for imaging analysis, temporal convolutional networks for time-series sensor data, and an attention-based fusion mechanism for dynamic multimodal integration. These techniques are supported by preprocessing methods such as U-Net++ segmentation and temporal feature extraction to enhance data consistency and efficiency. Additionally, lightweight models are deployed on IoMT devices to ensure scalability and real-time inference, making the framework practical for resource-constrained environments. Extensive evaluation demonstrates that the framework achieves 98% accuracy, 98% F1-score, and 0.99 AUC (area under the curve) of the ROC (receiver operating characteristic (AUC-ROC). These results show the proposed framework outperforms state-of-the-art approaches and showcases its potential for large-scale clinical deployment.

With the rapid development of technology, smart environments utilizing the Internet of Things, artificial intelligence, and big data are improving the quality of life and work efficiency through connected devices. However, these advances present significant security challenges. The data generated by these smart devices contains many private and sensitive information. In data transmission, crime and terrorism may intercept this sensitive information and use it for secret communications and illegal activities. Steganography hides information in media files and prevents information leakage and interception by criminal and terrorist networks in an intelligent environment. It is an important technology to protect data integrity and security. Traditional steganography techniques often cause detectable distortions, whereas Steganography Without Embedding (SWE) avoids direct modification of cover media, thereby minimizing detection risks. This paper introduces an innovative and robust technique called Robust Linked List (RLL)-SWE, which improves resistance to attacks compared to traditional methods. Using multiple median downsampling and gradient calculations, this method extracts stable features. It restructures them into a multi-head unidirectional linked list, ensuring accurate message retrieval and high resistance to adversarial attacks. Comprehensive analysis and simulation experiments confirm the technique’s exceptional effectiveness and steganographic capacity.

Integrating diverse biomedical knowledge information is essential to enhance the accuracy and efficiency of medical diagnoses, facilitate personalized treatment plans, and ultimately improve patient outcomes. However, Biomedical Information Integration (BII) faces significant challenges due to variations in terminology and the complex structure of entity descriptions across different datasets. A critical step in BII is biomedical entity alignment, which involves accurately identifying and matching equivalent entities across diverse datasets to ensure seamless data integration. In recent years, Large Language Model (LLMs), such as Bidirectional Encoder Representations from Transformers (BERTs), have emerged as valuable tools for discerning heterogeneous biomedical data due to their deep contextual embeddings and bidirectionality. However, different LLMs capture various nuances and complexity levels within the biomedical data, and none of them can ensure their effectiveness in all heterogeneous entity matching tasks. To address this issue, we propose a novel Two-Stage LLM construction (TSLLM) framework to adaptively select and combine LLMs for Biomedical Information Integration (BII). First, a Multi-Objective Genetic Programming (MOGP) algorithm is proposed for generating versatile high-level LLMs, and then, a Single-Objective Genetic Algorithm (SOGA) employs a confidence-based strategy is presented to combine the built LLMs, which can further improve the discriminative power of distinguishing heterogeneous entities. The experiment utilizes OAEI's entity matching datasets, i.e., Benchmark and Conference, along with LargeBio, Disease and Phenotype datasets to test the performance of TSLLM. The experimental findings validate the efficiency of TSLLM in adaptively differentiating heterogeneous biomedical entities, which significantly outperforms the leading entity matching techniques.

The convergence of transformative technologies, including the Internet of Things (IoT), Big Data, and Artificial Intelligence (AI), has driven private edge cloud systems to the forefront of research efforts. The access to massive terminals and the emergence of personalized services pose serious challenges for efficient resource management in power private edge cloud systems. To address the challenge of inequitable resource allocation in the private edge cloud, this work proposes an intelligent resource allocation strategy with a slicing and auction approach. By formalizing the resource allocation problem as a Mixed Integer Nonlinear Programming (MINLP) puzzle, the method transforms it into a hierarchical allocation challenge for Mobile Network Operators (MNOs), Mobile Virtual Network Operators (MVNOs), and power terminals. The proposed Multi-hop Progressive Auction Algorithm (MPAA) addresses the sliced resource allocation problem between MNOs and MVNOs. Furthermore, a Terminal Resource Allocation Strategy (TRAS) based on improved particle swarm optimization is proposed to solve the spectrum resource allocation problem between MVNOs and power terminals. Extensive simulation results show that the bidding overhead of MPAA is reduced by 6.12% and the average terminal satisfaction of TRAS is improved by about 1.3% compared to conventional methods, thus improving the utilization of wireless resources within the power AIoT.

Federated learning (FL) has emerged as a pivotal technology for the Internet of Things (IoT) that models distributed client data without compromising privacy. The IoT-based wearable generates data and FL running on a private edge performing human activity recognition (HAR). In this article, we proposed a novel technique to protect sensitive data during the training process and ensure the confidentiality of model updates before transmission to the edge server. The proposed technique integrates the El-Gamal encryption technique for data protection, and the FL process is rigorously optimized using pruning, quantization, and network slicing. Pruning removes redundant connections, which reduces model complexity and communication delays. On the other hand, quantization decreases the bit precision of model parameters, and network slicing strategically allocates resources solely for FL resulting in low latency and optimal bandwidth utilization. The results are evaluated in terms of accuracy and communication overhead, which is highly required in real-world applications. Furthermore, the HAR system within PEC shows better results by achieving an accuracy of 99% at 300 epochs that outperformed existing machine learning (ML) algorithms.

In today’s world, fake news on social media is a universal trend and has severe consequences. There has been a wide variety of countermeasures developed to offset the effect and propagation of Fake News. The most common are linguistic-based techniques, which mostly use deep learning (DL) and natural language processing (NLP). Even government-sponsored organizations spread fake news as a cyberwar strategy. In literature, computational-based detection of fake news has been investigated to minimize it. The initial results of these studies are good but not significant. However, we argue that the explainability of such detection, particularly why a certain news item is detected as fake, is a vital missing element of the studies. In real-world settings, the explainability of the system’s decisions is just as important as its accuracy. This article explores explainable fake news detection and proposes a sentence-comment-based co-attention sub-network model. The proposed model uses user comments and news contents to mutually apprehend top-$k$explainable check-worthy user comments and sentences for detecting fake news. The experimental result on real-world datasets shows that our proposed model outperforms state-of-the-art techniques by 5.56% in the$F$1 score. In addition, our model outperforms other baselines by 16.4% in normalized cumulative gain (NDCG) and 22.1% in Precision in identifying top-$k$comments from users, which indicates why a news article can be fake.

The vehicle-road cooperative perception needs high accuracy and real-time automatic background filtering to separate background objects from foreground objects in complex traffic scenes. Reducing the influence of foreground objects to improve accuracy, and introducing a new framework to improve real-time performance are two main challenges in automatic background filtering. This paper proposes an Automatic Background Filtering method with innovative Frame Selection and Background Matrix Extraction modules (ABF-FSBME) to address these challenges. Firstly, a new space division method with equal hitting probability is proposed to divide the 3D point cloud formed by roadside Light Detection and Ranging (LiDAR), which can reduce the influence of slight LiDAR vibrations. Secondly, the terminal-edge-cloud framework is introduced to balance delay-constrained tasks and computation-intensive tasks in automatic background filtering. Thirdly, a variance-based frame selection strategy with a sliding window mechanism is proposed to select candidate frames with fewer foreground objects. This strategy can reduce the influence of foreground objects in a coarse-grained way. Meanwhile, a new background matrix extraction method is proposed to construct the background matrix. This method can further reduce the influence of foreground objects in a fine-grained way. Finally, based on the extracted background matrix from a cloud server, the edge server can filter the raw frame in real-time. The experimental results show that the proposed ABF-FSBME method has better accuracy than other methods in error rate and integrity rate. Besides, the proposed ABF-FSBME can complete fame filtering within 10ms, and has almost no network delay, so it can satisfy the real-time requirement.

The rapid adoption of Internet of Things (IoT) devices in healthcare has introduced new challenges in preserving data privacy, security and patient safety. Traditional approaches need to ensure security and privacy while maintaining computational efficiency, particularly for resource-constrained IoT devices. This paper proposes a novel hybrid approach by combining federated learning and blockchain technology to provide a secured and privacy-preserved solution for IoT-enabled healthcare applications. Our approach leverages a public-key cryptosystem that provides semantic security for local model updates, while blockchain technology ensures the integrity of these updates and enforces access control and accountability. The federated learning process enables a secure model aggregation without sharing sensitive patient data. We implement and evaluate our proposed framework using EMNIST datasets, demonstrating its effectiveness in preserving data privacy and security while maintaining computational efficiency. The results suggest that our hybrid approach can significantly enhance the development of secure and privacy-preserved IoT-enabled healthcare applications, offering a promising direction for future research in this field.

The future communication technologies like 6G are capable to provide higher mobility and better quality-of-service requirements to Internet of Things (IoT). To ensure mobility, the 6G technologies need more reliable and scalable solutions, which are capable to integrate large-scale heterogeneous IoT networks. In a heterogeneous environment, seamless mobility along with the demands of IP addresses requires a proxy mobile IPv6 (PMIPv6) protocol that provides cost-effective solutions in next-generation IoT networks. The PMIPv6 has been exploited for resource efficiency in IoT-enabled next-generation networks. In this article, we have proposed a demand-based resource-efficient location-aware PMIPv6 extension for seamless mobility in the next-generation IoT networks. The proposed approach efficiently utilizes the network resources using location information and received signal strength (RSS). This solution enhances the performance of the PMIPv6 protocol in terms of signaling cost, and load on network entities. Furthermore, mathematical models are derived in terms of signaling cost and load distribution. The proposed solution is compared with the existing RSS-based PMIPv6 extension protocols. The results show that the proposed scheme enhances the performance and is a resource-friendly for the next-generation large-scale IoT networks.

A fundamental expectation of the stakeholders from the Industrial Internet of Things (IIoT) is its trustworthiness and sustainability to avoid the loss of human lives in performing a critical task. A trustworthy IIoT-enabled network encompasses fundamental security characteristics such as trust, privacy, security, reliability, resilience and safety. The traditional security mechanisms and procedures are insufficient to protect these networks owing to protocol differences, limited update options, and older adaptations of the security mechanisms. As a result, these networks require novel approaches to increase trust-level and enhance security and privacy mechanisms. Therefore, in this paper, we propose a novel approach to improve the trustworthiness of IIoT-enabled networks. We propose an accurate and reliable supervisory control and data acquisition (SCADA) network-based cyberattack detection in these networks. The proposed scheme combines the deep learning-based Pyramidal Recurrent Units (PRU) and Decision Tree (DT) with SCADA-based IIoT networks. We also use an ensemble-learning method to detect cyberattacks in SCADA-based IIoT networks. The non-linear learning ability of PRU and the ensemble DT address the sensitivity of irrelevant features, allowing high detection rates. The proposed scheme is evaluated on fifteen datasets generated from SCADA-based networks. The experimental results show that the proposed scheme outperforms traditional methods and machine learning-based detection approaches. The proposed scheme improves the security and associated measure of trustworthiness in IIoT-enabled networks.

The Internet of Medical Things (IoMT) effectively tackles several shortcomings of conventional healthcare systems. It includes medical personnel shortages, patient care quality, insufficient medical supplies, and healthcare expenditures. There are several advantages of using IoMT technology for enhanced treatment efficiency and quality, thus improving patient health. However, the frequency and magnitude of cyberattacks on IoMT are increasing at a breakneck pace. Therefore, this article proposes a cyberattack detection method for IoMT-based networks using ensemble learning and fog-cloud architecture to address security issues. The ensemble technique employs a set of long short-term memory (LSTM) networks as individual learners at the first level and stacks a decision tree on top of them to classify attack and normal events. In addition, we present a framework for deploying the proposed IoMT-based approach as Infrastructure as a Service in the cloud and Software as a Service in the fog. The proposed method is evaluated on the telemetry datasets of IoT and IIoT sensors (ToN-IoT) dataset, and the outcomes reveal that it surpasses the baseline approaches in terms of precision by 4%.

With the development of Internet of Things (IoT)-enabled Maritime Transportation Systems (MTS), massive data generated in the system not only requires to be stored reliably and cheaply, but also needs to be analyzed timely. The Cloud-based Maritime Transportation Systems (CMTS) allow users to upload the data without worrying about the price, capacity, location and so on. However, CMTS also brings some security issues, where the integrity protection of outsourced data is one of the most important issues since it is crucial for the safety, reliability and efficiency of sea lanes. To solve this problem, we propose an identity-based dynamic data integrity auditing scheme for CMTS. Our scheme decreases the burden of key management and improves the auditing efficiency by batch auditing. Besides, our scheme also supports dynamic operations on the outsourced data for CMTS. The security analysis shows that our scheme can ensure the feature of storage correctness and resist common attacks. In addition, the performance comparison results with other related schemes show that our scheme not only has the lowest computational cost on all entities, but also greatly reduces the communication overhead of the auditing phase. Therefore, our scheme is very suitable for data integrity verification in CMTS.

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.

Industry 5.0 has developed in full swing, and accelerated the process of the sixth-generation (6G) mobile communication. Physical layer security is important for complex 6G mobile communication networks. To process active complex events in 6G mobile cooperative networks, predicting secrecy performance in time is essential for the mobile communication quality evaluation. Using amplify-and-forward (AF) relaying, we propose a transmit antenna selection (TAS) based secrecy scheme in this article. To analyze the security of 6G mobile cooperative networks, signal-to-noise ratio of the end-to-end link is used to derive the novel expressions for secrecy outage probability (SOP). The theoretical results are confirmed by simulation results. Then, we use SOP as the important merit to evaluate the secrecy performance, and set up the dataset. To achieve secrecy performance prediction, a convolutional neural network (CNN) based SOP prediction algorithm is proposed. The designed CNN model has five convolution layers, which all use the same convolution in the padding and do not change data size. For this improved CNN structure, we adopt the idea of SqueezeNet, which belongs to the lightweight CNN. The improved CNN model can greatly reduce the parameters and network complexity on the premise of ensuring the prediction accuracy. We also examine the following state-of-the-art techniques, first, Elman, second, InceptionNet, third, deep neural network (DNN), and fourth, support vector machine methods. The proposed CNN algorithm can achieve better SOP prediction results than other existing methods. In particular, compared with DNN method, the prediction accuracy is increased by 66.7%.

Autonomous driving is considered one of the killer technologies in the intelligent era. The information transmission between autonomous vehicles and pedestrians (V2P) is very important to reduce the number of road accidents. Ambient backscatter communication (AmBC) technology can be used to increase the vehicle (or driver) awareness regarding the presence of pedestrians in a crosswalk to realize short-distance transmission of emergency messages. On the other hand, highly secure transmission in V2P networks is required to assure the broadcasting of emergency messages. However, the artificial noise scheme by an additional noise source to improve the physical layer security (PLS) is not suitable due to the dynamic vehicles. Therefore, in this paper, we propose a source-noise assisted AmBC transmission scheme in the V2P system, in which the noise is created by the ambient radio frequency (RF) source to improve the PLS performance. The closed-form expressions for the outage probability of the legitimate user and the intercept probability of eavesdropper are derived. Finally, the diversity gain performance of the system is studied by analyzing the asymptotic behaviors. The theoretical and simulation results show that the proposed scheme improves the system security performance at the expense of system reliability by utilizing the proposed source-noise aided scheme in the V2P network. Besides, the optimal reflection coefficient is related to the power allocation ratio for the signal of the reader and interference noise. The different reflection coefficient is required for achieving the best system performance under different power allocation ratios. Moreover, the results also show that there are error floors for the outage probability, depending on the reflection coefficient. Furthermore, the performance results show that the intercept probability will significantly decrease when the outage probability is fixed in the proposed scheme.

The Internet of Things (IoT) is considered a key enabler for a wide range of smart applications. In IoT, a large number of heterogeneous devices form anad hocconnection with each other. Thead hocinfrastructure is considered an integral part of IoT-empowered applications because of its efficient, cost-effective, and dynamic nature. These networks need to ensure the quality of service using their limited resources, particularly in multihop communication. Because multihop communication can be an easy target of attackers, it needs a secure and reliable data transmission scheme. In this article, we propose a secured and reliable continuous transmission scheme for cognitive hybrid automatic repeat request (HARQ)-aided IoT (SRCT-HARQ) capable of maintaining high throughput and lower delay. The SRCT-HARQ scheme is analytically modeled using a probability-based approach. The mathematical formulas are derived for delay and throughput using a probability-based analysis, and the results are verified using the Monte Carlo simulations. The performance results elaborate that the network throughput and delay are improved, mainly due to the proposed authentication scheme. Using our experimental results, we evaluated the optimal time for data transmission to protect the legal rights of primary users that resulted in improved performance.

Internet of Medical Things (IoMT) is an application-specific extension of the generalized Internet of Things (IoT) to ensure reliable communication among devices$C_{i}$, designed for the medical industry. However, a challenging issue associated with these networks, i.e., IoMT and IoT, is to ensure the authenticity of both source and destination modules and further guarantee the integrity of the multimodal data in the emergencies such as the COVID-19 pandemic. Various mechanisms for device authentication have been presented in the literature to resolve both devices and data’s authenticity, integrity, and privacy. Still, authentication of mobile device-to-server (in both homogeneous and heterogeneous IoMT) is not explicitly addressed for the black-hole attack. In this article, a device-to-server andvice versamutual authentication scheme are presented to ensure secure communication sessions among numerous mobile devices$C_{i}$and server$S_{j}$in the operational IoMT. The proposed scheme is a hybrid of medium access control (MAC) and enhanced on-demand vector (EAODV)-enabled routing schemes. In the proposed scheme, an offline phase is introduced to complete the registration process of member devices with the concerned server module. It blocks every possible entry of the potential intruder devices$A_{k}$in the operational IoMT. A mobile device$C_{i}$interested in initiating a communication session with a particular server$S_{j}$is needed to pass the mutual authentication process. As a result, only registered devices$C_{i}$are allowed to communicate. Additionally, a reliable encryption and decryption scheme is used to ensure data reliability during these communication sessions. Simulation results verify the exceptional performance of the proposed mutual authentication scheme in terms of authenticity, security, and integrity of both devices and data in the operational IoMT.

Industry 5.0 is the digitalization, automation and data exchange of industrial processes that involve artificial intelligence, Industrial Internet of Things (IIoT), and Industrial Cyber-Physical Systems (I-CPS). In healthcare, I-CPS enables the intelligent wearable devices to gather data from the real-world and transmit to the virtual world for decision-making. I-CPS makes our lives comfortable with the emergence of innovative healthcare applications. Similar to any other IIoT paradigm, I-CPS capable healthcare applications face numerous challenging issues. The resource-constrained nature of wearable devices and their inability to support complex security mechanisms provide an ideal platform to malevolent entities for launching attacks. To preserve the privacy of wearable devices and their data in an I-CPS environment, we propose a lightweight mutual authentication scheme. Our scheme is based on client-server interaction model that uses symmetric encryption for establishing secured sessions among the communicating entities. After mutual authentication, the privacy risk associated with a patient data is predicted using an AI-enabled Hidden Markov Model (HMM). We analyzed the robustness and security of our scheme using BurrowsAbadiNeedham (BAN) logic. This analysis shows that the use of lightweight security primitives for the exchange of session keys makes the proposed scheme highly resilient in terms of security, efficiency, and robustness. Finally, the proposed scheme incurs nominal overhead in terms of processing, communication and storage and is capable to combat a wide range of adversarial threats.

Industrial Internet of Things (IIoT) ensures reliable and efficient data exchanges among the industrial processes using Artificial Intelligence (AI) within the cyber-physical systems. In the IIoT ecosystem, devices of industrial applications communicate with each other with little human intervention. They need to act intelligently to safeguard the data confidentiality and devices' authenticity. The ability to gather, process, and store real-time data depends on the quality of data, network connectivity, and processing capabilities of these devices. Pervasive Edge Computing (PEC) is gaining popularity nowadays due to the resource limitations imposed on the sensor-embedded IIoT devices. PEC processes the gathered data at the network edge to reduce the response time for these devices. However, PEC faces numerous research challenges in terms of secured communication, network connectivity, and resource utilization of the edge servers. To address these challenges, we propose a secured and intelligent communication scheme for PEC in an IIoT-enabled infrastructure. In the proposed scheme, forged identities of adversaries, i.e., Sybil devices, are detected by IIoT devices and shared with edge servers to prevent upstream transmission of their malicious data. Upon Sybil attack detection, each edge server executes a parallel Artificial Bee Colony (pABC) algorithm to perform optimal network configuration of IIoT devices. Each edge server performs the job migration to their neighboring servers for load balancing and better network performance, based on their processing and storage capabilities. The experimental results justify the efficiency of our proposed scheme in terms of Sybil attack detection, the convergence curves of our pABC algorithm, delay, throughput, and control overhead of data communication using PEC for IIoT.