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.

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.

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 awareness of edge computing is attaining eminence and is largely acknowledged with the rise of Internet of Things (IoT). Edge-enabled solutions offer efficient computing and control at the network edge to resolve the scalability and latency-related concerns. Though, it comes to be challenging for edge computing to tackle diverse applications of IoT as they produce massive heterogeneous data. The IoT-enabled frameworks for Big Data analytics face numerous challenges in their existing structural design, for instance, the high volume of data storage and processing, data heterogeneity, and processing time among others. Moreover, the existing proposals lack effective parallel data loading and robust mechanisms for handling communication overhead. To address these challenges, we propose an optimized IoT-enabled big data analytics architecture for edge-cloud computing using machine learning. In the proposed scheme, an edge intelligence module is introduced to process and store the big data efficiently at the edges of the network with the integration of cloud technology. The proposed scheme is composed of two layers: IoT-edge and Cloud-processing. The data injection and storage is carried out with an optimized MapReduce parallel algorithm. Optimized Yet Another Resource Negotiator (YARN) is used for efficiently managing the cluster. The proposed data design is experimentally simulated with an authentic dataset using Apache Spark. The comparative analysis is decorated with existing proposals and traditional mechanisms. The results justify the efficiency of our proposed work.

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%.

Data integrity and authenticity are among the key challenges faced by the interacting devices of Internet of Things (IoT). The resource-constrained nature of sensor-embedded devices makes it even more difficult to design lightweight security schemes for these networks. In view of limited resources of the IoT devices, this article proposes a lightweight and trustworthy device-to-server mutual authentication scheme for edge-enabled IoT networks. Initially, a trusted authority generates and assigns identities (IDs) and mask them to servers and clients, also known as member devices, in an offline phase. These IDs are utilized to prevent possible infiltration of the adversary device(s). Next, every device ensures the authenticity of requesting devices using a sophisticated challenge, which is encrypted using a 128-b secret key,$\lambda _{i}$. Each device expects a reply from the intended destination device for resolving the encrypted challenge within the defined timeframe,$i.e., \bigtriangleup T$. Moreover, authenticity of the requesting device is verified through the stored IDs, which are shared in the offline phase. Simulation results have verified the exceptional performance of the proposed authentication scheme against field proven approaches in terms of computational and communication costs.

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.

The Internet of Things (IoT) plays an important role to connect people, data, processes, and things. From linked supply chains to big data produced by a large number of IoT devices to industrial control systems where cybersecurity has become a critical problem in IoT‐powered systems. Denial of Service (DoS), distributed denial of service (DDoS), and ping of death attacks are significant threats to flying networks. This paper presents an intrusion detection system (IDS) based on attack probability using the Markov chain to detect flooding attacks. While the paper includes buffer queue length by using queuing theory concept to evaluate the network safety. Also, the network scenario will change due to the dynamic nature of flying vehicles. Simulation describes the queue length when the ground station is under attack. The proposed IDS utilizes the optimal threshold to make a tradeoff between false positive and false negative states with Markov binomial and Markov chain distribution stochastic models. However, at each time slot, the results demonstrate maintaining queue length in normal mode with less packet loss and high attack detection.