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The increasing reliance on Medical Internet of Things (MIoT) devices introduces critical cybersecurity vulnerabilities, necessitating advanced, adaptive defense mechanisms. Recent cyber incidents—such as compromised critical care systems, modified therapeutic device outputs, and fraudulent clinical data inputs—demonstrate that these threats now directly impact life-critical aspects of patient security. In this paper, we introduce a machine learning-enabled Cognitive Cyber-Physical System (ML-CCPS), which is designed to identify and respond to cyber threats in MIoT environments through a layered cognitive architecture. The system is constructed on a feedback-looped architecture integrating hybrid feature modeling, physical behavioral analysis, and Extreme Learning Machine (ELM)-based classification to provide adaptive access control, continuous monitoring, and reliable intrusion detection. ML-CCPS is capable of outperforming benchmark classifiers with an acceptable computational cost, as evidenced by its macro F1-score of 97.8% and an AUC of 99.1% when evaluated with the ToN-IoT dataset. Alongside classification accuracy, the framework has demonstrated reliable behaviour under noisy telemetry, maintained strong efficiency in resource-constrained settings, and scaled effectively with larger numbers of connected devices. Comparative evaluations, radar-style synthesis, and ablation studies further validate its effectiveness in real-time MIoT environments and its ability to detect novel attack types with high reliability.

The Internet of Things (IoT) refers to a network where different smart devices are interconnected through the Internet. This network enables these devices to communicate, share data, and exert control over the surrounding physical environment to work as a data-driven mobile computing system. Nevertheless, due to wireless networks' openness, connectivity, resource constraints, and smart devices' resource limitations, the IoT is vulnerable to several different routing attacks. Addressing these security concerns becomes crucial if data exchanged over IoT networks is to remain precise and trustworthy. This study presents a trust management evaluation for IoT devices with routing using the cryptographic algorithms Rivest, Shamir, Adleman (RSA), Self-Adaptive Tasmanian Devil Optimization (SA_TDO) for optimal key generation, and Secure Hash Algorithm 3-512 (SHA3-512), as well as an Intrusion Detection System (IDS) for spotting threats in IoT routing. By verifying the validity and integrity of the data exchanged between nodes and identifying and thwarting network threats, the proposed approach seeks to enhance IoT network security. The stored data is encrypted using the RSA technique, keys are optimally generated using the Tasmanian Devil Optimization (TDO) process, and data integrity is guaranteed using the SHA3-512 algorithm. Deep Learning Intrusion detection is achieved with Convolutional Spiking neural network-optimized deep neural network. The Deep Neural Network (DNN) is optimized with the Archimedes Optimization Algorithm (AOA). The developed model is simulated in Python, and the results obtained are evaluated and compared with other existing models. The findings indicate that the design is efficient in providing secure and reliable routing in IoT-enabled, futuristic, smart vertical networks while identifying and blocking threats. The proposed technique also showcases shorter response times (209.397 s at 70% learn rate, 223.103 s at 80% learn rate) and shorter sharing record times (13.0873 s at 70% learn rate, 13.9439 s at 80% learn rate), which underlines its strength. The performance metrics for the proposed AOA-ODNN model were evaluated at learning rates of 70% and 80%. The highest metrics were achieved at an 80% learning rate, with an accuracy of 0.989434, precision of 0.988886, sensitivity of 0.988886, specificity of 0.998616, F-measure of 0.988886, Matthews Correlation Coefficient (MCC) of 0.895521, Negative predictive value (NPV) of 0.998616, False Positive Rate (FPR) of 0.034365, and False Negative Rate (FNR) of 0.103095.

The Smart Grid (SG) heavily depends on the Advanced Metering Infrastructure (AMI) technology, which has shown its vulnerability to intrusions. To effectively monitor and raise alarms in response to anomalous activities, the Intrusion Detection System (IDS) plays a crucial role. However, existing intrusion detection models are typically trained on cloud servers, which exposes user data to significant privacy risks and extends the time required for intrusion detection. Training a high-quality IDS using Artificial Intelligence (AI) technologies on a single entity becomes particularly challenging when dealing with vast amounts of distributed data across the network. To address these concerns, this paper presents a novel approach: a fog-edge-enabled Support Vector Machine (SVM)-based federated learning (FL) IDS for SGs. FL is an AI technique for training Edge devices. In this system, only learning parameters are shared with the global model, ensuring the utmost data privacy while enabling collaborative learning to develop a high-quality IDS model. The test and validation results obtained from this proposed model demonstrate its superiority over existing methods, achieving an impressive percentage improvement of 4.17% accuracy, 13.19% recall, 9.63% precision, 13.19% F1 score when evaluated using the NSL-KDD dataset. Furthermore, the model performed exceptionally well on the CICIDS2017 dataset, with improved accuracy, precision, recall, and F1 scores reaching 6.03%, 6.03%, 7.57%, and 7.08%, respectively. This novel approach enhances intrusion detection accuracy and safeguards user data and privacy in SG systems, making it a significant advancement in the field.

When it comes to smart healthcare business systems, network-based intrusion detection systems are crucial for protecting the system and its networks from malicious network assaults. To protect IoMT devices and networks in healthcare and medical settings, our proposed model serves as a powerful tool for monitoring IoMT networks. This study presents a robust methodology for intrusion detection in Internet of Medical Things (IoMT) environments, integrating data augmentation, feature selection, and ensemble learning to effectively handle IoMT data complexity. Following rigorous preprocessing, including feature extraction, correlation removal, and Recursive Feature Elimination (RFE), selected features are standardized and reshaped for deep learning models. Augmentation using the BAT algorithm enhances dataset variability. Three deep learning models, Transformer-based neural networks, self-attention Deep Convolutional Neural Networks (DCNNs), and Long Short-Term Memory (LSTM) networks, are trained to capture diverse data aspects. Their predictions form a meta-feature set for a subsequent meta-learner, which combines model strengths. Conventional classifiers validate meta-learner features for broad algorithm suitability. This comprehensive method demonstrates high accuracy and robustness in IoMT intrusion detection. Evaluations were conducted using two datasets: the publicly available WUSTL-EHMS-2020 dataset, which contains two distinct categories, and the CICIoMT2024 dataset, encompassing sixteen categories. Experimental results showcase the method’s exceptional performance, achieving optimal scores of 100% on the WUSTL-EHMS-2020 dataset and 99% on the CICIoMT2024.

With the increased sophistication of cyber-attacks, there is a greater demand for effective network intrusion detection systems (NIDS) to protect against various threats. Traditional NIDS are incapable of detecting modern and sophisticated attacks due to the fact that they rely on pattern-matching models or simple activity analysis. Moreover, Intelligent NIDS based on Machine Learning (ML) models are still in the early stages and often exhibit low accuracy and high false positives, making them ineffective in detecting emerging cyber-attacks. On the other hand, improved detection and prediction frameworks provided by ensemble algorithms have demonstrated impressive outcomes in specific applications. In this research, we investigate the potential of ensemble models in the enhancement of NIDS functionalities in order to provide a reliable and intelligent security defense. We present a NIDS hybrid model that uses ensemble ML techniques to identify and prevent various intrusions more successfully than stand-alone approaches. A combination of several distinct machine learning methods is integrated into a hybrid framework. The UNSW-NB15 dataset is pre-processed, and its features are engineered prior to being used to train and evaluate the proposed model structure. The performance evaluation of the ensemble of various ML classifiers demonstrates that the proposed system outperforms individual model approaches. Using all the employed experimental combination forms, the designed model significantly enhances the detection accuracy attaining more than 99%, while false positives are reduced to less than 1%.

Blockchain technologies (BCT) are utilized in healthcare to facilitate a smart and secure transmission of patient data. BCT solutions, however, are unable to store data produced by IoT devices in smart healthcare applications because these applications need a quick consensus process, meticulous key management, and enhanced eprivacy standards. In this work, a smart and secure eHealth framework SSEHCET (Smart and Secure EHealth Framework using Cutting-edge Technologies) is proposed that leverages the potentials of modern cutting-edge technologies (IoT, 5G, mobile edge computing, and BCT), which comprises six layers: 1) The sensing layer-WBAN consists of medical sensors that normally are on or within the bodies of patients and communicate data to smartphones. 2) The edge layer consists of elements that are near IoT devices to collect data. 3) The Communication layer leverages the potential of 5G technology to transmit patients' data between multiple layers efficiently. 4) The storage layer consists of cloud servers or other powerful computers. 5) Security layer, which uses BCT to transmit and store patients' data securely. 6) The healthcare community layer includes healthcare professionals and institutions. For the processing of medical data and to guarantee dependable, safe, and private communication, a Smart Agent (SA) program was duplicated on all layers. The SA leverages the potential of BCT to protect patients' privacy when outsourcing data. The contribution is substantiated through a meticulous evaluation, encompassing security, ease of use, user satisfaction, and SSEHCET structure. Results from an in-depth case study with a prominent healthcare provider underscore SSEHCET's exceptional performance, showcasing its pivotal role in advancing the security, usability, and user satisfaction paradigm in modern eHealth landscapes.

Medical image authentication plays a vital role in secure healthcare industries, where assuring the integrity and authenticity of diagnostic images is critical for safe clinical decisions. This study presents a robust, dual watermarking framework that embeds a machine-readable QR code and a hospital logo into medical images using a hybrid frequency-domain method combining Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT). A lightweight Convolutional Neural Network (CNN) decoder is developed for efficient watermark extraction, optimized through a novel enhanced loss function that integrates Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Sobel edge loss. The encoder-decoder framework ensures imperceptibility, low computational cost, and resilience to standard signal and geometric attacks. The model is tested against Salt & Pepper noise, median filtering, rotation, and cropping to validate robustness. The proposed scheme achieves high watermark extraction fidelity with a Peak Signal-to-Noise Ratio (PSNR) ranging from 64.87 to 68.75 dB and Normalized Correlation (NC) values consistently reaching 1.0 under several attacks, demonstrating an average improvement of 28–35% in PSNR and 12–15% in NC. Furthermore, the lightweight CNN demonstrates a small model size of 0.65 MB with real-time inference capability, making it suitable for embedded and resource-constrained medical devices. The results confirm that the proposed dual watermarking method maintains visual quality, structural integrity, and security of medical images while ensuring efficient and accurate watermark retrieval.

The proliferation of Internet of Medical Things (IoMT) devices in e-Health systems has shown improved healthcare delivery but introduced severe cybersecurity vulnerabilities, including spoofing, denial-of-service, and data breaches. This study proposes leveraging artificial intelligence (AI) for an Intrusion Detection System (IDS) to secure IoMT environments and further assist in real-time threat detection and resilience of e-Health systems. This provided an improved model that implemented feature importance and ensemble learning, as well as contributed to developing a new hybrid system that uses the pre-trained Decision Tree (C4.5) model that incorporates a pre-trained Decision Tree (C4.5) model into the RL loop using Deep Q-Networks (DQN). This hybrid framework exploits the efficiency and low latency of pre-trained C4.5 for initial classification, and enables the ability of the system to learn dynamically from network interactions, adapt to changing patterns of attack, and improve detection performance over time. The general framework employs SMOTE to address class imbalance, while focal loss is utilized as an evaluation tool to analyze the classifiers’ focus on hard-to-classify and minority class samples. It is important to note that the hybrid IDS has exhibited higher accuracy compared to Decision Tree - C4.5 with total rewards maximized, indicating the adaptive learning and stability in changing environments. The proposed model achieved an accuracy of 99.03% for binary classes, 98.55% for the five classes, and 99.56% for the 14-class experiment when using the initial classification with the Decision Tree (C4.5) model on the Canadian Institute for Cybersecurity, Internet of Medical Things-2024(CICIoMT2024) dataset. The initial classification and latency results are additionally compared to a few other lightweight classifiers such as Random Forest, XGBoost, and Simple Neural Networks. To bring adaptability and dynamic threat detection of Deep Reinforcement Learning (DRL) classifiers, the C4.5 model was integrated into a DQN framework to address evolving network threats over time. The hybrid model also persisted with improved performance, measuring 99.20% accuracy for the binary classes with CICIoMT2024 dataset. Proposed IDS was also evaluated for its generalization capability across heterogeneous datasets, i-e, WUSTL-EHMS, ECU-IoHT, DF_IOMT, and CICIOT23. The model consistently achieved high detection performance across the datasets and outperformed their respective previously achieved results with the C4.5 supervised classifier, which verified its robustness and flexibility across different IoMT contexts. The proposed hybrid IDS is therefore validated as a deployment-aware, lightweight, and adaptive framework capable of effective intrusion detection in dynamic healthcare settings that are resource-limited and demand real-time responsiveness.

This paper addresses the computational methods and challenges associated with prime number generation, a critical component in encryption algorithms for ensuring data security. The generation of prime numbers efficiently is a critical challenge in various domains, including cryptography, number theory, and computer science. The quest to find more effective algorithms for prime number generation is driven by the increasing demand for secure communication and data storage and the need for efficient algorithms to solve complex mathematical problems. Our goal is to address this challenge by presenting two novel algorithms for generating prime numbers: one that generates primes up to a given limit and another that generates primes within a specified range. These innovative algorithms are founded on the formulas of odd-composed numbers, allowing them to achieve remarkable performance improvements compared to existing prime number generation algorithms. Our comprehensive experimental results reveal that our proposed algorithms outperform well-established prime number generation algorithms such as Miller-Rabin, Sieve of Atkin, Sieve of Eratosthenes, and Sieve of Sundaram regarding mean execution time. More notably, our algorithms exhibit the unique ability to provide prime numbers from range to range with a commendable performance. This substantial enhancement in performance and adaptability can significantly impact the effectiveness of various applications that depend on prime numbers, from cryptographic systems to distributed computing. By providing an efficient and flexible method for generating prime numbers, our proposed algorithms can develop more secure and reliable communication systems, enable faster computations in number theory, and support advanced computer science and mathematics research.

Planned and targeted attacks, such as the advanced persistent threat (APT), are highly sophisticated forms of attack. They involve numerous steps and are intended to remain within a system for an extended length of period before progressing to the next stage of action. Anticipating the next behaviors of attackers is a challenging and crucial task due to the stealthy nature of advanced attack scenarios, in addition to the possible high volumes of false positive alerts generated by different security tools such as intrusion detection systems (IDSs). Intelligent models that are capable of establishing a correlation individual between individual security alerts in order to reconstruct attack scenarios and to extract a holistic view of intrusion activities are required to exploit hidden links between different attack stages. Federated learning models performed in distributed settings have achieved successful and reliable implementations. Alerts from distributed security devices can be utilized in a collaborative manner based on several learning models to construct a federated model. Therefore, we propose an intelligent detection system that employs federated learning models to identify advanced attack scenarios such as APT. Features extracted from alerts are preprocessed and engineered to produce a model with high accuracy and fewer false positives. We conducted training on four machine learning models in a centralized learning; these models are XGBoost, Random Forest, CatBoost, and an ensemble learning model. To maintain privacy and ensure the integrity of the global model, the proposed model has been implemented using conventional neural network federated learning (CNN_FL) across several clients during the process of updating weights. The experimental findings indicate that ensemble learning achieved the highest accuracy of 88.15% in the context of centralized learning. CNN_FL has demonstrated an accuracy of 90.18% in detecting various attacks of APTs while maintaining a low false alarm rate.