As the Internet of Things (IoT) continues its rapid expansion, cloud computing has become integral to various smart healthcare applications. However, the proliferation of digital health services raises significant concerns regarding security and data privacy, making the protection of sensitive medical information paramount. To effectively tackle these challenges, it is crucial to establish resilient network infrastructure and data storage systems capable of defending against malicious entities and permitting access exclusively to authorized users. This requires the deployment of a robust authentication mechanism, wherein medical IoT devices, users (such as doctors or nurses), and servers undergo registration with a trusted authority. The process entails users retrieving data from the cloud server, while IoT devices collect patient data. Before granting access to data retrieval or storage, the cloud server verifies the authenticity of both the user and the IoT device, ensuring secure and authorized interactions within the system. With millions of interconnected smart medical IoT devices autonomously gathering and analyzing vital patient data, the importance of robust security measures becomes increasingly evident. Standard security protocols are fundamental in fortifying smart healthcare applications against potential threats. To confront these issues, this paper introduces a secure and resource-efficient cloud-enabled authentication mechanism. Through empirical analysis, it is demonstrated that our authentication mechanism effectively reduces computational and communication overheads, thereby improving overall system efficiency. Furthermore, both informal and formal analyses affirm the mechanism's resilience against potential cyberattacks, highlighting its effectiveness in safeguarding smart healthcare applications.

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.

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%.

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.

Handwritten signature verification is challenging because there is a huge variation between the orientation thickness and appearance of handwritten signatures. A strong signature verification system is essential to refine the accuracy of confirming user authentication. This investigation introduces an inclusive framework for training and evaluating hybrid vision transformer models on diverse signature datasets, aiming to refine the accuracy in confirming user authentication. In previous studies, transformer & MobileNet were used for computer vision classification and signature verification separately. Drawing inspiration from the Convolutional Neural Network (CNN), the hybrid model is proposed as a deep-learning model (ResNet-18 & MobileNetV2) with the Vision Transformer model (proposed method 1 & proposed method 2).To bring originality to this study, we excluded the final layer of the feature extractor and smoothly integrated it with the initial layer of the vision transformer. In the scope of this research, we introduced a unique hybrid vision transformer model. Furthermore, we incorporated swish and tangent hyperbolic (tanh) activation functions into the validation model to enhance its performance. Experimental results showcase the effectiveness of the proposed hybrid model, achieving notable accuracies on various datasets, including 92.33% accuracy on Bhsig-Bengali, 99.89% accuracy on Bhsig-Hindi, 99.96% accuracy on Cedar, and 74.09% accuracy on UTsig-Persian datasets, respectively. The practical implications of this research extend to real-time signature verification for secure and efficient user authentication, particularly in mobile applications. This advancement in signature verification technology presents new possibilities for practical use in diverse scenarios beyond academia.