The rapid expansion of the Healthcare Internet-of-Things (HIoT) has created new opportunities for delivering personalized, real-time medical intelligence. In such systems, IoT devices acquire data at the point of care, edge servers provide low-latency preprocessing and lightweight inference, cloud platforms perform large-scale model training and optimization, and user interfaces enable clinical decision support and feedback. Designing an effective HIoT framework requires addressing a multi-objective trade-off: maximizing accuracy A ( θ ) and stability S ( θ ) while minimizing latency L ( θ ) and energy E ( θ ) , subject to constraints on model size | θ | ≤ M max and robustness S ( θ ) ≥ δ . To address this challenge, we propose HiPER , a hierarchical optimization-driven HIoT framework that jointly integrates acquisition, analytics, intelligence, and user interaction. To demonstrate its practical utility, HiPER is applied to monkeypox (Mpox) detection, referred to as HiPER-Mpox . In this framework, the edge employs lightweight inference with privacy-preserving transformations, while the cloud leverages transfer learning using a NASNetMobile backbone with squeeze-and-excitation channel recalibration to enhance accuracy and generalization. The user layer provides interpretable outputs and incorporates clinician feedback, improving trust and robustness. Evaluation on the Mpox Skin Lesion Dataset (MSLD) shows that HiPER-Mpox achieves 96% accuracy, 93% precision, MCC of 0.9145, and Kappa of 0.911, with an average per-image latency of 554 ms and a compact 2.07 MB edge model. These results demonstrate that the proposed HIoT framework satisfies the formulated optimization objectives while delivering a practical, interpretable, and resource-efficient solution for emerging healthcare challenges.

The increasing prevalence of adversarial evasion techniques poses a significant challenge to the reliability of intrusion detection systems (IDSs) in Internet-of-Things (IoT) environments. Minor manipulations of traffic telemetry can alter temporal behaviour or frequency-level patterns, leading to degraded detection performance and potential security failures. This study presents the Multi-Frequency Temporal Spatio-Transformer (MFTST), a computational framework designed to improve adversarial robustness in IoT intrusion detection. MFTST combines temporal Transformer-based encoding with multi-frequency channel attention to capture both sequential traffic behaviour and frequency-level variations in learned traffic representations. The framework is evaluated using a Composite attack that emulates the detection-side effects of two representative defense-evasion behaviours: obfuscation (T1027) and indicator tampering (T1070), as commonly described in the MITRE ATT&CK knowledge base. The Composite attack jointly perturbs temporal attention behaviour and frequency-domain traffic representations to assess the resilience of MFTST against timing-based masking, attention disruption, and frequency-domain perturbations. To improve robustness, the model is trained using a composite adversarial optimization strategy that jointly considers classification performance, temporal-attention stability, and frequency-domain perturbation control. Experimental evaluations on MQTTset and X-IIoTID demonstrate that MFTST improves detection performance under both clean and adversarial conditions. The results show that adversarial training improves multiclass accuracy from 0.8314 to 0.8558 on MQTTset and from 0.8758 to 0.9192 on X-IIoTID. Under stronger adversarial evaluation, MFTST also maintains higher robustness than conventional machine-learning, deep-learning, and robustness-oriented baselines. These findings indicate that temporally conditioned frequency attention can improve both detection reliability and interpretability in adversarial IoT security monitoring.

Crowdfunding is an important mechanism for supporting innovative projects by connecting creators with distributed contributors. Prior research has identified persistent limitations in both traditional and blockchain-based crowdfunding platforms, including limited transparency, centralized control, passive contributor roles, and inflexible fund management processes. These limitations hinder accountability, equitable participation, and effective decision-making throughout the campaign lifecycle. This paper presents a blockchain-enabled crowdfunding framework designed as a decision-support artifact for adaptive fund allocation and participatory governance. The framework enables contributors to engage in spending-request governance through Quadratic Voting, which balances influence across heterogeneous financial stakes and mitigates dominance by large contributors. To support adaptive campaign management, the framework further integrates Ethereum smart contracts with a Markov Decision Process (MDP), enabling campaign-level decisions to respond to evolving contribution patterns and campaign states. The framework is implemented and evaluated through controlled experiments on the Sepolia Ethereum test network. The evaluation includes both an internal ablation of Quadratic Voting and MDP-based adaptive support and an external comparison against representative blockchain-based baselines. The results show that the combined Quadratic Voting and MDP design achieves lower approval latency and higher throughput than partial or static variants of the framework, and that the full proposed platform outperforms the compared baseline systems under increasing campaign workload. Overall, the study demonstrates how participatory governance, adaptive decision support, and transparent smart-contract execution can be systematically integrated into crowdfunding platforms, providing practical guidance for the design of scalable, efficient, and accountable decentralized crowdfunding systems.

Context: Software vulnerability detection (SVD) is increasingly challenged by the scale and complexity of modern software systems. Although deep learning–based approaches demonstrate strong detection performance, their adoption in security-critical settings is limited by insufficient interpretability, adversarial resilience, and systematic Responsible AI integration. Objective: This paper aims to design and evaluate a Responsible AI–driven framework for SVD that operationalizes fairness, interpretability, security, reliability, and transparency without compromising detection effectiveness. Method: We propose a model-agnostic vulnerability detection framework that incorporates fairness-aware data preprocessing, multi-model evaluation, and structured verification mechanisms. Interpretability is achieved through multi-view explanations combining global and local SHAP, LIME, and attention-based attribution. Explanation consistency is examined using attention-guided token occlusion, while security is evaluated via multiple white-box adversarial attacks on correctly classified test samples. The framework is validated on three public datasets—CWE-119, CWE-399, and DiverseVul—using deep learning and pre-trained code models. Results: Experimental results demonstrate competitive detection performance across datasets while providing structured explanation and adversarial evaluation evidence. Interpretability analyses reveal dataset-specific vulnerability cues aligned with domain knowledge, and perturbation studies show consistent confidence shifts under controlled token masking. Adversarial experiments further illustrate stable performance trends under attack conditions. Conclusion: The findings indicate that Responsible AI principles can be systematically operationalized within deep learning–based SVD pipelines. By integrating fairness, interpretability, security evaluation, reliability assessment, and transparency mechanisms, the proposed framework supports trustworthy and security-aware deployment of AI-driven vulnerability detection systems.

Federated Learning (FL) operates as a distributed software system in which a central coordinator orchestrates training across heterogeneous and intermittently available clients. In practice, client selection and aggregation policies are configured as static and independent parameters, which become brittle under fluctuating computation capacity, network latency, and client reliability. Consequently, FL systems often suffer from slow convergence, unstable training, and limited adaptability under dynamic execution conditions. This work formulates FL orchestration as a software architecture problem and introduces FedJoint , a modular framework that jointly coordinates client selection and aggregation timing. Unlike prior approaches that optimize these mechanisms independently, FedJoint treats them as coupled runtime control decisions within a unified architecture. The objective is to enable adaptive co-management that improves efficiency, robustness, and operational adaptability under heterogeneous and non-stationary conditions. FedJoint is realized as a modular orchestration architecture composed of three decoupled components: a Selection Manager controlling client participation, an Aggregation Manager governing update integration, and a Deep Reinforcement Learning (DRL) Controller that adapts orchestration decisions based on runtime feedback. The components interact through well-defined interfaces that support configurability, substitution, and integration with existing FL platforms. Within this architecture, a DRL–based policy adapts selection and aggregation parameters to balance accuracy, latency, client dropout, and communication overhead under dynamic execution conditions. Evaluation on CIFAR-10 and MNIST under two heterogeneity levels (Dirichlet α ∈ { 0 . 1 , 0 . 5 } ) and non-stationary conditions shows final accuracy of 63.62%–97.82%, with 5.2–14.0 × speedup over synchronous and semi-asynchronous baselines. Compared to RL-based baselines, FedJoint achieves up to 12.2 × lower wall-clock cost at higher final accuracy. Ablation confirms that gains stem from joint coordination, with single-component variants showing accuracy gaps up to 31.84 percentage points. Treating FL orchestration as a coupled architectural concern enables more robust and manageable distributed learning systems and offers concrete guidance for adaptive FL platform design.

The evolution of Industry 5.0 introduces complex, temporally irregular network behaviors, challenging intrusion detection in heterogeneous industrial environments. Existing Intrusion Detection Systems (IDSs) often struggle with asynchronous traffic and lack interpretability. To overcome these limitations, we propose AegisNet, a novel sequence-based IDS that reformulates intrusion detection as a sequence classification task on fixed-length windows ofTconsecutive observations (sequence length), augmented with explicit time-delta features to capture irregular traffic intervals. AegisNet introduces a novel Temporal-Aware and Self-Adaptive Multi-Head Attention (TA-SAMHA) mechanism. It introduces learnable temporal bias projections to directly encode irregular time intervals into the attention process, enabling the system to capture fine-grained temporal dependencies and evolving behaviors. It also employs a Dual-Gate Residual Encoder (DGRE) for bidirectional sequence modeling with dual gating, enabling enriched contextual representations and stable learning across deep temporal layers. Additionally, we introduce the IDS-specific adaptation of Temporal SHAP (T-SHAP), enabling time-resolved feature attributions that expose how influential factors shift during sequential intrusion detection, offering a new dimension of temporally aware explainability for evolving attack patterns. Experimental results demonstrate that AegisNet achieves a detection accuracy of 99.63% on the CICIoV2024 dataset and 99.13% on the CICIoT2023 dataset, demonstrating its effectiveness for securing Industrial environments.

Early detection and proper treatment of epilepsy is essential and meaningful to those who suffer from this disease. The adoption of deep learning (DL) techniques for automated epileptic seizure detection using electroencephalography (EEG) signals has shown great potential in making the most appropriate and fast medical decisions. However, DL algorithms have high computational complexity and suffer low accuracy with imbalanced medical data in multi seizure-classification task. Motivated from the aforementioned challenges, we present a simple and effective hybrid DL approach for epileptic seizure detection in EEG signals. Specifically, first we use a K-means Synthetic minority oversampling technique (SMOTE) to balance the sampling data. Second, we integrate a 1D convolutional neural network (CNN) with a Bidirectional Long Short-Term Memory (BiLSTM) network based on Truncated Backpropagation Through Time (TBPTT) to efficiently extract spatial and temporal sequence information while reducing computational complexity. Finally, the proposed DL architecture uses softmax and sigmoid classifiers at the classification layer to perform multi and binary seizure-classification tasks. In addition, the 10-fold cross-validation technique is performed to show the significance of the proposed DL approach. Experimental results using the publicly available UCI epileptic seizure recognition data set shows better performance in terms of precision, sensitivity, specificity, and F1-score over some baseline DL algorithms and recent state-of-the-art techniques.

Deterministic and low-latency communication enabled by Time-Sensitive Networking (TSN) is essential for modern large-scale segmented industrial networks, which are typically divided into multiple operational domains. However, securing such multi-domain, functionally segmented TSN environments remains challenging due to resource-constrained edge devices, strict real-time requirements, and uneven availability of labeled attack data across isolated domains. To address these challenges, we propose a Selective Federated Intrusion Detection Framework with Domain Adaptation (SAFID) tailored for large-scale segmented industrial networks. The framework introduces a cluster-aware domain classification strategy that identifies data-rich (active) and data-scarce (passive) domains based on Quality-of-Service (QoS) flow density. Active domains collaboratively train TDNet, a compact neural network architecture optimized for TSN traffic, through federated learning. TDNet employs low-rank factorized dense layers to reduce computational overhead and parameter count while preserving classification accuracy. To further minimize latency and memory consumption, a post-training quantization step compresses both weights and activations. Passive domains receive the globally aggregated model and locally fine-tune it via a lightweight domain-specific adaptation mechanism to capture localized threats. A post-training quantization step further compresses TDNet for deployment in latency-critical industrial environments. Extensive evaluation of SAFID demonstrates detection accuracies of 99.75% on CICIDS2017 and 95.75% on Edge-IIoTset. The framework also achieves a 6.5× improvement in inference throughput after quantization and exhibits robust generalization across diverse domain-specific attack patterns. Unlike prior methods treating federated intrusion detection uniformly, our framework enables selective participation and targeted adaptation, making it highly suitable for real-time, resource-constrained multi-domain TSN networks.

The rapid expansion of IoT networks has increased the risk of cyber threats, making intrusion detection systems (IDS) critical for maintaining security. However, most of the existing IDS rely on computationally intensive deep learning architectures, rendering them unsuitable for IoT environments with limited resources. Additionally, existing IDS approaches, including those using Knowledge Distillation (KD), often fail to capture the complex temporal dependencies and contextual relationships inherent in IoT traffic, which limits their ability to detect complex multi-stage attacks. Furthermore, these models frequently lack transparency, hindering effective decision-making by security experts. To address these gaps, we propose DistillGuard, a novel IDS framework designed specifically for IoT networks. The proposed framework employs a Transformer-based teacher model, which utilizes a hybrid attention mechanism combining multi-head self-attention (MHSA) and cross-attention layers to effectively capture both temporal and contextual patterns in network traffic. The framework further incorporates a Selective Gradient-Based Knowledge Distillation (SG-KD) process to transfer critical knowledge from the teacher model to a lightweight student model, optimizing performance while reducing computational costs. In addition,’DistillGuard’ integrates gradient contribution heatmaps, layer-wise contribution, and gradient selection impact analysis to provide detailed explanability, enabling security experts to understand which layers contribute to the detection of attacks. Experimental results demonstrate that’DistillGuard’ achieves superior detection accuracy and efficiency compared to existing state-of-the-art IDS models.

The Software-Defined Networking (SDN) powered Internet of Things (IoT) offers a global perspective of the network and facilitates control and access of IoT devices using a centralized high-level network approach called Software Defined-IoT (SD-IoT). However, this integration and high flow of data generated by IoT devices raises serious security issues in the centralized control intelligence of SD-IoT. Motivated by the aforementioned challenges, we present a new Deep-Learning empowered Threat Hunting Framework named DLTHF to protect SD-IoT data and detect (binary and multi-vector) attack vectors. First, an automated unsupervised feature extraction module is designed that combines data perturbation-driven encoding and normalization-driven scaling with the proposed Long Short-Term Memory Contractive Sparse AutoEncoder (LSTMCSAE) method to filter and transform dataset values into the protected format. Second, using the encoded data, a novel Threat Detection System (TDS) using Multi-head Self-attention-based Bidirectional Recurrent Neural Networks (MhSaBiGRNN) is designed to detect cyber threats and their types. In particular, a unique TDS strategy is developed in which each time instances is analyzed and allocated a self-learned weight based on the degree of relevance. Further, we also design a deployment architecture for DLTHF in the SD-IoT network. The framework is rigorously evaluated on two new SD-IoT data sources to show its effectiveness.

The increasing adoption of Industrial Internet of Things (IIoT) networks has introduced new security challenges, particularly to ensure software security against evolving malware threats. IIoT systems rely on interconnected edge, cloud, and embedded devices, which are highly vulnerable to malware attacks that exploit software vulnerabilities, propagate across networks, and compromise industrial operations. However, existing malware detection approaches often struggle with resource constraints, high-dimensional feature spaces, and the need for real-time adaptability, making them inefficient for large-scale IIoT deployments. To address these challenges, this paper presents "GWPSO-GAMD," a resource-efficient malware detection framework designed to enhance software security in IIoT networks. The framework integrates a hybrid metaheuristic feature selection algorithm—Grey Wolf Optimization and Particle Swarm Optimization (GWPSO)—to identify the most discriminative and computationally efficient features, reducing processing overhead while maintaining high detection accuracy. These features are then analyzed by the Graph Android Malware Detector (GAMD), which leverages graph convolutional networks (GCNs) and attention mechanisms to model malware propagation behaviors and uncover complex attack patterns in IIoT environments. Empirical evaluations on two open-source malware datasets, CIC-MalDroid-2020 and CIC-MalMem-2022, demonstrate that the proposed model achieves detection accuracy above 98.41% while significantly reducing CPU usage by 47%, memory footprint by 57%, and training time by 67%. The results highlight GWPSO-GAMD’s ability to provide scalable, real-time malware detection for resource-constrained IIoT systems, advancing the vision of secure, intelligent, and resource-aware IIoT networks.

The rapid proliferation of Internet of Things (IoT) devices has transformed modern infrastructures, yet their inherently distributed and dynamic nature poses significant challenges for cybersecurity. Traditional intrusion detection systems (IDS) often rely on static models or linear temporal analysis, which makes them insufficient to identify and respond to evolving threats that manifest across both spatial and temporal dimensions. Furthermore, existing attention-based mechanisms tend to treat spatial and temporal dependencies separately and apply fixed attention weights, limiting their adaptability in complex IoT environments. To address these limitations, we propose, a novel IDS framework incorporating a Spatio-Temporal Conditioning with Dynamic Multi-Head Attention (STC-MHA-DW) mechanism. This module captures contextual interdependencies across both spatial features and temporal windows by leveraging head-wise adaptive projections, enabling the model to dynamically reweight attention based on surrounding threat context. The temporal encoder is built using a dual-stage gated mechanism that processes both short-term fluctuations and long-term dependencies, while the attention layer refines representations through localized spatio-temporal salience. We also introduce a scalable, cloud-native deployment architecture using microservices and containerization to ensure efficient performance under dynamic network loads. Experimental evaluations show that the proposed ids achieves the highest detection accuracy of 99.84%, precision of 99.47%, recall of 98.83%, and f1-score of 99.14% with a very low false alarm rate, outperforming existing models.

School teachers, both experienced and novice, are bound to follow the predesigned K-12 curriculum focusing primarily on theoretical content knowledge. They have only limited opportunities to get acquainted with experiential teaching methods incorporating practical laboratory experiments. Deficiency of practical knowledge upskill programs predominantly affects teaching competence in subjects like basic electronics. Fostering electronics teaching competency is often ignored despite the higher significance of electronics. Further, there is a scarcity of research studies on the effectiveness of practical electronics training for school teachers. Against this backdrop, this paper explores the impact of a hands-on training cum experimentation program for school teachers organized by the IEEE Education Society (EdSoc) Kerala Chapter. Titled as ‘School Teachers' Electronics Practicals Upskilling Program (STEP-UP),‘ it envisioned upskilling school teachers of Kerala, a southern state in India. The STEP-UP was focused on basic electronics engineering for day-to-day applications. To study the impact of STEP-UP on school teachers, we used the Kirkpatrick model, an established method for evaluating training programs. The impact assessment of the training program is deliberated based on the revised Kirkpatrick model with the integration of STEP-UP keywords. It was inferred from the study that school teachers are interested in actively participating in practical skill development programs. Moreover, teachers' degree of involvement emphasizes the potential of such programs in enhancing teaching quality rooted in experiential learning. The paper ends with offering a few suggestions and recommendations in accordance with the research findings on the impact of STEP-UP.

The engineering education today emphasizes the need to combine book learning with real-world application. However, much of the research done by undergraduates, which could be very valuable, is scattered and not fully used. To address this, a new tool called “AI-based Research Companion (ARC)” has been developed. ARC leverages advanced Generative AI technology, including GPT-4, to systematically organize, enhance, and offer personalized recommendations for undergraduate research projects. This platform is more than a simple tool; it aims to inspire undergraduates to dive into research by making the process approachable and engaging, thus increasing participation in research activities. Initial assessments of ARC have revealed an encouraging rise in student engagement with research, indicating a shift towards more research-oriented projects. The integration of GPT-4 within ARC stands out significantly; it precisely addresses the detailed demands of undergraduate research by providing a tailored, intelligent exploration pathway. By incorporating GPT-4's advanced features with a user-centric design, ARC emerges as an innovative platform, emphasizing the pivotal role of Generative AI in enhancing and expanding undergraduate research initiatives.

This paper presents the System for Emotion and Engagement Recognition in Education (SEERE), a cutting-edge advancement integrating computer vision and deep learning tech-nologies to evaluate real-time student engagement through facial emotion recognition and eye tracking. SEERE, a transformative educational tool built on the robust YOLO V8 architecture, customizes the FER2013 dataset, making use of meticulously annotated emotion and eye position data. It goes further, es-tablishing a unique ‘concentration metric,’ a quantitative index of student engagement, bridging a gap in modern responsive teaching approaches. Higher concentration metrics signal height-ened student engagement, offering educators real-time data to adjust teaching techniques and feedback accordingly. The paper provides a thorough review of facial emotion recognition models, setting the stage for understanding the innovative strides made by SEERE. Detailed discussions on the prototype's design and architecture are followed by initial experimental results, reinforcing the system's validity and potential.

The increasing adoption of Unmanned Aerial Ve-hicles (UAV s) in various critical applications necessitates robust security measures to protect these systems from cyber threats. In response, this research introduces an innovative Intrusion Detection System (IDS) specifically tailored for UAV s. The proposed IDS leverages Hierarchical Attention-based Long Short-Term Memory (H-LSTM) networks to effectively model the intricate temporal dependencies in UAV data. This architecture allows for comprehensive surveillance of UAV behavior, capturing both short-term anomalies and long-term deviations from expected patterns. The hierarchical attention mechanism enables the system to focus on salient features within the data, enhancing detection accuracy and robustness. To address the critical need for interpretable AI in cybersecurity, we incorporate Shapley Ad-ditive Explanations (SHAP) into our IDS. SHAP values provide a coherent and intuitive explanation of the IDS's decisions by emphasizing the specific features and their contributions to the intrusion detection process. The performance of the proposed system is rigorously evaluated using the N-BaIoT dataset. Our experiments demonstrate that the H-LSTM-based IDS outper-forms traditional methods, achieving a higher detection rate while minimizing false positives. Moreover, the incorporation of SHAP explanations facilitates rapid incident analysis, allowing security professionals to discern between genuine threats and benign anomalies effectively.

The exponential growth of Internet of Things (IoT) devices poses distinctive challenges to safeguarding the security and privacy of interconnected systems. As the frequency of cyberattacks continues to escalate, the development of an effective and scalable Intrusion Detection System (IDS) based on Federated Learning (FL) for IoT becomes increasingly complex. Existing methodologies struggle to balance spatial and temporal feature extraction, particularly when confronted with dynamic and evolving cyber threats. Additionally, the lack of diversity in datasets employed for FL-based IDS evaluations further hinders progress. Furthermore, a notable tradeoff between performance and scalability emerges, particularly as the number of edge devices in communication grows. Motivated by the aforementioned challenges, this article presents a horizontal FL model that combines Convolutional Neural Networks (CNN) and Bidirectional Long-Term Short Memory (BiLSTM) for effective intrusion detection. This hybrid approach aims to address the limitations of existing methods and enhance the effectiveness of intrusion detection in the context of FL for IoT. Specifically, CNN plays a pivotal role in spatial feature extraction, allowing the model to identify and comprehend local patterns indicative of potential intrusions, and the BiLSTM component contributes by capturing temporal dependencies and learning sequential patterns within the data. The proposed IDS adheres to a zero-trust model by keeping the data on local edge devices, sharing only the learned weights with the centralized FL server. In turn, the FL server aggregates updates from diverse sources to optimize the accuracy of the global learning model. The experimental results using CICIDS2017 and Edge-IIoTset prove the effectiveness of the proposed approach over centralized and federated deep learning-based IDS.

The Internet of Things (IoT) has revolutionized various sectors by enabling seamless device interaction. However, the proliferation of IoT devices has also raised significant security and privacy concerns. Traditional security measures often fail to address these concerns due to the unique characteristics of IoT networks, such as heterogeneity, scalability, and resource constraints. This survey paper adopts a thematic exploration approach for a comprehensive analysis to investigate the convergence of quantum computing, federated learning, and 6G wireless networks. This novel intersection is explored to significantly improve security and privacy within the IoT ecosystem. To enable several secure, intelligent IoT applications, quantum computing, with its superior computational capabilities, can strengthen encryption algorithms, making IoT data more secure. Federated learning, a decentralized machine learning approach, allows IoT devices to learn a shared model while keeping all the training data on the original device, thereby enhancing privacy. This synergy becomes even more crucial when integrated with the high-speed, low-latency capabilities of 6G networks, which can facilitate real-time, secure data processing and communication among many IoT devices. Second, we discuss the latest developments, offering an up-to-date overview of advanced solutions, available datasets, and key performance metrics and summarizing the vital insights, challenges, and trends in securing IoT systems. Third, we design a conceptual framework for integrating quantum computing in federated learning, adapted for 6G networks. Finally, we highlight the future advancements in quantum technologies and 6G networks and summarize the implications for IoT security, paving the way for researchers and practitioners in the field of IoT security.

The Industrial Internet of Things (IIoT) is a collection of interconnected smart sensors and actuators with industrial software tools and applications. IIoT aims to enhance manufacturing and industrial processes by capturing and analyzing real-time industrial data. However, the heterogeneous and homogeneous nature of IIoT networks makes them vulnerable to several security threats. As data is transmitted over an insecure communication medium, intruders may intercept communication among different entities and perform malicious activities. Consequently, ensuring the security and privacy of data transmitted in IIoT networks is essential. Motivated by the aforementioned challenges, this article presents a deep-learning-integrated blockchain framework for securing IIoT networks. Specifically, first, we design a private blockchain-based secure communication among the IIoT entities using session-based mutual authentication and key agreement mechanism. In this approach, the Proof-of-Authority (PoA) consensus mechanism is used for verification of the transactions and block creation based on the voting of miners over the cloud server. Second, we design a novel deep-learning-based intrusion detection system that combines contractive sparse autoencoder (CSAE), attention-based bidirectional long short-term memory (ABiLSTM) networks, and softmax classifier for cyberattack detection. The practical implementation of blockchain and deep-learning techniques proves the effectiveness of the proposed framework.

The Internet of Medical Things (IoMT) has revolutionized healthcare, but its vulnerabilities demand robust security solutions, especially for resource-constrained devices. In this research, we introduce an innovative Software as a Service (SaaS)-based Intrusion Detection System (IDS) designed specifically for the unique challenges of IoMT, deploying at the edge for enhanced efficiency. Our proposed IDS incorporates a multi-faceted approach: Firstly, it leverages the Particle Swarm Optimization (PSO) algorithm for feature engineering, optimizing data representation to reduce computational overhead on resource-constrained devices. Secondly, a diverse ensemble of machine learning and deep learning models is employed to detect a wide array of intrusion attempts within IoMT networks. Thirdly, interpretation is achieved using SHapley Additive exPlanations (SHAP), providing transparency and understanding of the decision-making process. By combining intelligence, efficiency, explainability, and deploying as a SaaS solution at the network edge, our IDS not only bolsters the security of resource-constrained IoMT devices but also empowers healthcare professionals with actionable insights, ensuring patient data privacy and network integrity in this dynamic and critical domain. Finally, the results using a publicly available healthcare dataset namely WUSTL-EHMS-2020 proves the effectiveness of the proposed IDS over some recent state-of-the-art works.

The Industrial Internet of Things (IIoT) is rapidly evolving, and with this evolution, cyber threats have become a significant issue. IIoT networks, despite improving service quality, are uniquely vulnerable to security threats due to their inherent connectivity and the use of low-power devices. Traditional Deep Learning-based IDS, while accurate, suffer from a “black box” issue that hides the reasoning behind their decisions, leading to a decrease in user trust. To address this, our research presents an Explainable and intelligent mechanism for data-efficient intrusion detection in IIoT. Our proposed IDS enhances data efficiency by employing a Bidirectional Long-Short Term Memory (BiLSTM) model with a self-adaptive attention mechanism. The selfadaptive attention mechanism is a novel feature of our IDS framework, designed specifically for IIoT environments. This mechanism dynamically adjusts its focus to prioritize critical elements within a dataset, allocating more computational resources to data segments likely to contain patterns or anomalies indicative of security threats. When integrated with BiLSTM, which excels at capturing temporal dependencies, the mechanism enhances the IDSs ability to learn efficiently from limited datasets. This focus on significant data features and temporal patterns reduces the need for extensive training datasets, making it particularly effective in IIoT settings where data may be sparse yet complex. In addition, we enhance the proposed IDSs transparency by incorporating the SHapley Additive exPlanations mechanism from Explainable AI, thereby boosting the IDSs trustworthiness and interpretability. Our system exhibits outstanding performance on benchmark datasets such as CICIDS2017 and X-IIoTID, attaining accuracies of 99.92% and 96.54%, respectively.

The deployment of Internet of Things (IoT) systems in Smart Agriculture (SA) operates in extreme environments including wind, snowfall, flooding, landscape, and so on for collecting and processing real-time data. The increased connectivity and broad adoption of IoT devices with low-power communications on farmland support farmers in making data-driven decisions using various Artificial Intelligence (AI) techniques. Furthermore, in such an environment, edge computing is also utilized to provide computationally intensive, latency-sensitive, and bandwidth-demanding services at the edge of the network. However, protecting edge-to-Things in the extreme environment of SA is challenging, due to the volume of data, and also attackers exploit network gateways to perform Distributed Denial of Service (DDoS) attacks. Motivated by the aforementioned challenges, we develop a novel deep learning-based Intrusion Detection System (IDS) for edge-envisioned SA in extreme environments. Specifically, a hybrid approach is developed by combining bidirectional gated recurrent unit, long-short term memory with softmax classifier to detect attacks at the edge of the network. To allow faster learning, the proposed IDS employs the Truncated Backpropagation through Time (TBPTT) approach to handle lengthy sequences of network data. Furthermore, we suggest an attack scenario with deployment architecture for the proposed IDS in the extreme environment of SA. Extensive experiments using three publicly available datasets namely, CIC-IDS2018, ToN-IoT, and Edge-IIoTset prove the effectiveness of the proposed IDS over some traditional and contemporary state-of-the-art techniques.

Vehicle Road Cooperation Systems (VRCS) use next-generation Internet technologies, including 5G, edge computing, and artificial intelligence to improve mobility, comfort, and travel efficiency. Internet of Vehicles (IoV) ecosystem serves as the technological backbone for VRCS by enabling seamless communication and data exchange between vehicles, infrastructure, and traffic management centers. This enables real-time, high-speed communication, efficient data processing, and enhanced security, fostering the development of autonomous driving, smart traffic management, and seamless connectivity within the VRCS ecosystem. At the same time, cyber attacks have become more complex, persistent, organized, and weaponized in IoV network. Threat Intelligence (TI) has emerged as a prominent security approach to obtain a complete view of the dynamically growing cyber threat environment. On the other hand, modeling TI is a challenging task due to the limited labels available for different cyber threat sources. Second, most of the available designs requires a large investment of resources and use hand-crafted features, making the entire process error-prone and time-consuming. To tackle these challenges, this paper presents TIMIF, a deep-learning-based threat intelligence modeling and identification framework for Intelligent IoV and is based on three key modules: first, the proposed TIMIF adopts an Automated Pattern Extractor (APE) module to extract hidden patterns from IoV networks. Employing its output, we design a TI-Based Detection (TIBD) module to detect abnormal behavior and TI-Attack Type Identification (TIATI) module to identify attack types. Extensive experiments are carried out on three different publicly intrusion data sources namely HCRL-car hacking, ToN-IoT and CICIDS-2017 to illustrate the utility of TIMIF framework over some commonly used baselines and state-of-the-art techniques.

Summary Artificial Intelligence (AI) based cyber threat detection tools are widely used to process and analyze a large amount of data for improved intrusion detection performance. However, these models are often considered as black box by the cybersecurity experts due to their inability to comprehend or interpret the reasoning behind the decisions. Moreover, AI‐based threat hunting is data‐driven and is usually modeled using the data provided by multiple cloud vendors. This is another critical challenge, as a malicious cloud can provide false information (i.e., insider attacks) and can degrade the threat‐hunting capability. In this paper, we present a blockchain‐enabled eXplainable AI (XAI) for enhancing the decision‐making capability of cyber threat detection in the context of Smart Healthcare Systems. Specifically, first, we use blockchain to validate and store data between multiple cloud vendors by implementing a Clique Proof‐of‐Authority (C‐PoA) consensus. Second, a novel deep learning‐based threat‐hunting model is built by combining Parallel Stacked Long Short Term Memory (PSLSTM) networks with a multi‐head attention mechanism for improved attack detection. The extensive experiment confirms its potential to be used as an enhanced decision support system by cybersecurity analysts.

The efficient patient-independent and interpretable framework for electroencephalogram (EEG) epileptic seizure detection (ESD) has informative challenges due to the complex pattern of EEG nature. Automated detection of ES is crucial, while Explainable Artificial Intelligence (XAI) is urgently needed to justify the model detection of epileptic seizures in clinical applications. Therefore, this study implements an XAI-based computer-aided ES detection system (XAI-CAESDs), comprising three major modules, including of feature engineering module, a seizure detection module, and an explainable decision-making process module in a smart healthcare system. To ensure the privacy and security of biomedical EEG data, the blockchain is employed. Initially, the Butterworth filter eliminates various artifacts, and the Dual-Tree Complex Wavelet Transform (DTCWT) decomposes EEG signals, extracting real and imaginary eigenvalue features using frequency domain (FD), time domain (TD) linear feature, and Fractal Dimension (FD) of non-linear features. The best features are selected by using Correlation Coefficients (CC) and Distance Correlation (DC). The selected features are fed into the Stacking Ensemble Classifiers (SEC) for EEG ES detection. Further, the Shapley Additive Explanations (SHAP) method of XAI is implemented to facilitate the interpretation of predictions made by the proposed approach, enabling medical experts to make accurate and understandable decisions. The proposed Stacking Ensemble Classifiers (SEC) in XAI-CAESDs have demonstrated 2% best average accuracy, recall, specificity, and F1-score using the University of California, Irvine, Bonn University, and Boston Children's Hospital-MIT EEG data sets. The proposed framework enhances decision-making and the diagnosis process using biomedical EEG signals and ensures data security in smart healthcare systems.

The integration of the Internet of Things (IoT) with traditional healthcare systems has improved quality of healthcare services. However, the wearable devices and sensors used in Healthcare System (HS) continuously monitor and transmit data to the nearby devices or servers using an unsecured open channel. This connectivity between IoT devices and servers improves operational efficiency, but it also gives a lot of room for attackers to launch various cyber-attacks that can put patients under critical surveillance in jeopardy. In this article, a Blockchain-orchestrated Deep learning approach for Secure Data Transmission in IoT-enabled healthcare system hereafter referred to as “BDSDT” is designed. Specifically, first a novel scalable blockchain architecture is proposed to ensure data integrity and secure data transmission by leveraging Zero Knowledge Proof (ZKP) mechanism. Then, BDSDT integrates with the off-chain storage InterPlanetary File System (IPFS) to address difficulties with data storage costs and with an Ethereum smart contract to address data security issues. The authenticated data is further used to design a deep learning architecture to detect intrusion in HS network. The latter combines Deep Sparse AutoEncoder (DSAE) with Bidirectional Long Short-Term Memory (BiLSTM) to design an effective intrusion detection system. Experiments on two public data sources (CICIDS-2017 and ToN-IoT) reveal that the proposed BDSDT outperformed state-of-the-arts in both non-blockchain and blockchain settings and have obtained accuracy close to 99% using both datasets.

Considering the efficiency and security of healthcare data processing, indiscriminate data collection, annotation, and transmission are unwise. In this article, we propose the normalized double entropy (NDE) method to assess image data quality in the form of metatask. In specific, the probability entropy and distance entropy are both adopted and normalized to evaluate the data quality. The experimental results show the stable ability of the NDE to distinguish good and bad data in terms of information contribution. Furthermore, the model's diagnostic performances driven by selected good and bad data are compared, and a clear gap exists between them under the premise of the same amount of data. Screening 70% of the dataset can achieve almost the same accuracy as that based on all data. This article focuses on healthcare data quality and data redundancy and provides a practical evaluation tool to facilitate the identification and collection of valuable data, which is beneficial to improve efficiency and protect cybersecurity in healthcare systems.

The recent burgeoning of Internet of Things (IoT) technologies in the maritime industry is successfully digitalizing Maritime Transportation Systems (MTS). In IoT-enabled MTS, the smart maritime objects, infrastructure associated with ship or port communicate wirelessly using an open channel Internet. The intercommunication and incorporation of heterogeneous technologies in IoT-enabled MTS brings opportunities not only for the industries that embrace it, but also for cyber-criminals. Cyber Threat Intelligence (CTI) is an effective security strategy that uses artificial intelligence models to understand cyber-attacks and can protect data of IoT-enabled MTS proficiently. Unsurprisingly, most of the existing CTI-based solutions uses manual analysis to extract relevant threat information, and has low detection and high false alarm rate. Therefore, to tackle aforementioned challenges, an automated framework called DLTIF is developed for modeling cyber threat intelligence and identifying threat types. The proposed DLTIF is based on three schemes: a deep feature extractor (DFE), CTI-driven detection (CTIDD) and CTI-attack type identification (CTIATI). The DFE scheme automatically extracts the hidden patterns of IoT-enabled MTS network and its output is used by CTIDD scheme for threat detection. The CTIATI scheme is designed to identify the exact threat types and to assist security analysts in giving early warning and adopt defensive strategies. The proposed framework has obtained upto 99% accuracy, and outperforms some traditional and recent state-of-the-art approaches.

In Internet of Things (IoT)-based e-Health Systems (IoTEHS), medical devices form a large network that continuously sense and share the healthcare data with the nearby edge devices or cloud servers. The health data is subsequently made available to various IoTEHS stakeholders (such as doctors, nurses and patients) to track and monitor patients under observation. However, the entire IoTEHS stakeholders communicate with each other over a wireless unsecured public communication channel. This is a major security and privacy loophole wherein the attacker can exploit the vulnerability of the system and can launch various attacks on the ongoing communication. Motivated by the aforementioned challenges, a secure data dissemination scheme using AI and blockchain is proposed. In this scheme, the transaction collected through healthcare sensors installed around the patients premises act as data sets that is forwarded to the nearby edge devices. The collected data is first filtered using AI-based intrusion detection system located at the edge of the network. Second, a secure health monitoring network is designed using blockchain. Specifically, the filtered or normal transactions are transmitted to centralized cloud servers where the smart contact-enabled consensus mechanism is used to validate the transactions. Once the transaction gets validated, it is stored on distributed InterPlanetary File System (IPFS) of cloud and returned transaction hash is stored on the blockchain ledger located at edge devices making data exchange faster. The detailed experimental investigation demonstrates that the proposed schemes are efficient (in terms of computing and processing time) as well as its resistance to a variety of security attacks.

The rapid development of the automotive Industrial Internet of Things requires secure networking infrastructure toward digitalization. Cybertwin (CT) is a next-generation networking architecture that serves as a communication, and digital asset owner, and can make the Vehicle-to-Everything (V2X) network flexible and secure. However, CT itself can publish end users’ digital assets to other entities as a service, making data security and privacy major obstacles in the realization of V2X applications. Motivated from the aforementioned discussion, this article presents BDTwin, a blockchain and deep-learning-based integrated framework to enhance security and privacy in CT-driven V2X applications. Specifically, a blockchain scheme is designed to ensure secure communication among vehicles, roadside units, CT-edge server, and cloud server using a smart contract-based enhance-Proof-of-Work (ePoW) and Zero Knowledge Proof (ZKP)-based verification process. Smart contracts are used to enforce rules and regulations that govern the behavior of V2X entities in a nondeniable and automated manner. In a deep-learning scheme, an autoregressive-deep variational autoencoder model is combined with attention-based bidirectional long short-term memory (A-BLSTM) for automatic feature extraction and attack detection by analyzing CT-edge servers data in a V2X environment. Security analysis and experimental results using two different sources, ToN-IoT and CICIDS-2017 show the superiority of the proposed BDTwin framework over some baseline and recent state-of-the-art techniques.

The industrial Internet of Things (IIoT) is a fast-growing network of Internet-connected sensing and actuating devices aimed to enhance manufacturing and industrial operations. This interconnection generates a high volume of data over the IIoT network and raises serious security (e.g., the rapid evolution of hacking techniques), privacy (e.g., adversaries performing data poisoning and inference attacks), and scalability issues. To mitigate the aforementioned challenges, this article presents, a new privacy-preserved threat intelligence framework (P2TIF) to protect confidential information and to identify cyber-threats in IIoT environments. There are two major elements in the proposed P2TIF framework. First, a scalable blockchain module that enables secure communication of IIoT data and prevents data poisoning attacks. Second, a deep learning module that transforms actual data into a new format and protects data from inference attacks using a deep variational autoencoder (DVAE) technique. The encoded data are then employed by a threat detection system using attention-based deep gated recurrent neural network (A-DGRNN) to recognize malicious patterns in IIoT environments. The proposed framework is validated using two different network data sources, i.e., ToN-IoT and IoT-Botnet. Security analysis and experimental results revealed the high efficiency and scalability of the proposed P2TIF framework.

The industrial healthcaresystem has enabled the possibility of realizing advanced real-time monitoring of patients and enriched the quality of medical services through data sharing among intelligent wearable devices and sensors. However, this connectivity brings the intrinsic vulnerabilities related to security and privacy due to the need of continuous communication and monitoring over public network (insecure channel). Motivated from the aforementioned discussions, we integrate permissioned blockchain and smart contract with deep learning (DL) techniques to design a novel secure and efficient data sharing framework named PBDL. Specifically, PBDL first has a blockchain scheme to register, verify (using zero-knowledge proof), and validate the communicating entities using the smart contract-based consensus mechanism. Second, the authenticated data are used to propose a novel DL scheme that combines stacked sparse variational autoencoder (SSVAE) with self-attention-based bidirectional long short term memory (SA-BiLSTM). In this scheme, SSVAE encodes or transforms the healthcare data into new format, and SA-BiLSTM identifies and improves the attack detection process. The security analysis and experimental results using IoT-Botnet and ToN-IoT datasets confirm the superiority of the PBDL framework over existing state-of-the-art techniques.

With the development of Internet of Vehicles (IoV), the integration of Internet of Things (IoT) and manual vehicles becomes inevitable in Intelligent Transportation Systems (ITS). In ITS, the IoVs communicate wirelessly with other IoVs, Road Side Unit (RSU) and Cloud Server using an open channel Internet. The openness of above participating entities and their communication technologies brings challenges such as security vulnerabilities, data privacy, transparency, verifiability, scalability, and data integrity among participating entities. To address these challenges, we present a Privacy-Preserving based Secured Framework for Internet of Vehicles (P2SF-IoV). P2SF-IoV integrates blockchain and deep learning technique to overcome aforementioned challenges, and works on two modules. First, a blockchain module is developed to securely transmit the data between IoV-RSU-Cloud. Second, a deep learning module is designed that uses the data from blockchain module to detect intrusion and its performance is assessed using two network datasets IoT-Botnet and ToN-IoT. In contrast with other peer privacy-preserving intrusion detection strategies, the P2SF-IoV approach is compared, and the experimental results reveal that in both blockchain and non-blockchain based solutions, the proposed P2SF-IoV framework outperforms.

Cooperative Intelligent Transport System (C-ITS) is a promising technology that aims to improve the traditional transport management systems. In C-ITS infrastructure Autonomous Vehicles (AVs) communicate wirelessly with other AVs, Road Side Units (RSUs) and Traffic Command Centres (TCCs) using an open channel Internet. However, the use of the Internet brings inherent vulnerabilities related to privacy (e.g., adversary performing inference and data poisoning attacks), and security (e.g., AVs can be compromised using advanced hacking techniques) issues and prevents the faster realization of C-ITS applications. To address these challenges, this paper presents a privacy-preserving-based secure framework to provide both privacy and security in C-ITS infrastructure. The proposed framework provides two level of security and privacy using blockchain and deep learning modules. First, a blockchain module is designed to securely transmit the C-ITS data between AVs–RSUs-TCCs, and a smart contract-based enhanced Proof of Work (ePoW) technique is designed to verify data integrity and mitigate data poisoning attacks. Second, a deep-learning module is designed that includes Long-Short Term Memory-AutoEncoder (LSTM-AE) technique for encoding C-ITS data into a new format to prevent inference attacks. The encoded data is used by the proposed Attention-based Recurrent Neural Network (A-RNN), for intrusive events recognition in C-ITS infrastructure. The proposed A-RNN is trained using Truncated Backpropagation Through Time (BPTT) algorithm. The framework is further validated and tested using two publicly available ToN-IoT and CICIDS-2017 datasets. The proposed framework is compared with peer privacy-preserving intrusion detection techniques, and the result shows the effectiveness of the proposed framework over several state-of-the-art techniques in both blockchain and non-blockchain systems.