In a wireless sensor network (WSN), data are transferred such that only intentional entities are capable of decoding and understanding them. Security is essential in the design of any data and processing-intensive schemes. Researchers have designed various models as well as security systems for providing secure data communications. Most of the developed schemes have individual nuances, benefits and shortcomings that make choosing a superior security scheme highly challenging for model designers. In blockchain technology, secret sharing methods enhance the security performance of a system. Here, the Blockchain_SecAuth approach is presented for secure data communication in multi-WSNs. The entities of interest are base station (BS), cluster head (CH) nodes, end users, blockchain and ordinary nodes. The phases involved are setup, CH and node registration, key generation, mutual authentication among nodes as well as among nodes and end users, authorization, access control, communication and node logout. The designed authorization approach is based upon security functions, namely, hashing operation, encryption, X-OR function and so forth. Moreover, the Blockchain_SecAuth approach attained minimal computation time and memory of approximately 0.463 s and 20.552 GHz, respectively, and a maximal detection rate of 87.641%. The proposed method is useful in environmental monitoring, smart agriculture, healthcare, smart cities, and industrial automation.

Efficient orchestration in the edge–cloud continuum is essential for reducing energy consumption and meeting latency requirements in large-scale IoT systems. This article presents a hybrid deep reinforcement learning (DRL) and federated learning (FL) framework that dynamically allocates computation across IoT, edge, fog, and cloud layers. The DRL agent learns energy-efficient scheduling strategies through a latency-aware reward design, while FL enables decentralized model training without exposing raw data. Experimental evaluation demonstrates up to 31.6% lower energy consumption and 28.4% latency reduction compared to existing heuristics. Results also show rapid learning convergence within 200 episodes, indicating strong adaptability under changing network and workload conditions. These findings confirm the effectiveness of the proposed framework in improving energy efficiency, latency performance, and scalability for next-generation IoT deployments.

Recently, unmanned aerial vehicles (UAVs) are widely deployed, due to their emerging benefits, like less cost, and on-demand installation. The exchanging of information is done amongst the UAVs. However, this kind of communication may contain security issues because of its vibrant network topology. For addressing these problems, this article devises an optimized model for attaining secure communication amongst UAVs. The quality of the link is predicted using Deep Q Network using a prediction agent. The establishment of effective routes is done with Tunicate Honey Badger Algorithm. The fitness is modeled with link quality and distance. The evaluation and monitoring agent is employed for data communication. The maintenance agent helps to manage the route failures. The decision-making agent utilizes Shepard Convolutional Neural Network (ShCNN) for detecting the malicious behaviors. The ShCNN employs certain parameters as input to detect attack. If attack is encountered, then defensive agent alleviates identified attack.

The recognition and categorization of ships is very significant for maritime security and defense board. The existing SAR image-oriented ship recognition techniques are not capable of real-time applications since the count of SAR sensors is sparse and the contrast of images is minimum. To cope up with certain issues, this article designs an efficient technique for ship detection and classification by introducing hybrid optimization-based deep learning technique. Here, segmentation is done using bounding box segmentation. The ships on the ocean surface are effectively detected using deep convolutional neural network, where the network is trained utilizing devised exponential mayfly optimization algorithm (EMO). The newly designed EMO is derived by the combination of the exponential weighted moving average (EWMA) and mayfly optimization algorithm (MA). The type of ships is classified effectively utilizing deep residual neural network (DRN) and the network is trained by introducing the proposed political exponential mayfly optimization algorithm (PoEMO).

In this manuscript, Evolutionary Gravitational Neocognitron Neural-Network-based Block chain Technology is proposed for a Secured Dynamic Optimal Routing in Wireless Sensor Networks (BT-SDOR-WSN-EGNNN). The purpose of block chain (BC) technology is to distribute the distributed routing information management depending on secure BC token transactions. The Proof-of-Authority (PoA)-based consensus process is selected for effectual transaction in BC wireless sensor networks. The Evolutionary Gravitational Neocognitron Neural Network (EGNNN) is considered to select salient nodes denote the features of node-based validators. The Evolutionary Gravitational Neocognitron Neural Network model enhances the collection of validators by the attributes corresponding to every node. Then, Trust-Based Secure Intelligent Opportunistic Routing Protocol (TBSIOP) is employed to assist routing nodes and makes more informed routing decisions and choose the dependable routing links. The proposed approach is simulated in Network Simulator (NS-2) tool. The performance metrics, such as average latency, average energy consumption, and throughput of blockchain token transactions, is examined. From the simulation, the proposed BT-SDOR-WSN-EGNNN method attains 76.26%, 65.57%, 48.99%, 42.9% lesser delay during 25% malicious routing environment, 73.06%, 63.82%, 59.25%, 38.84% lesser delay during 50% malicious routing environment analysed to the existing BT-SDOR-WSN-DCNN, BT-SDOR-WSN-MDP, BT-SDOR-WSN-RL and BT-SDOR-WSN-WL-DCNN methods.

Summary Blockchain technology is an effective way to protect data that is handled by wireless sensor network (WSN). The traditional authentication protocol relies on trusted third parties, and most of the WSN paradigm does not allow these conditions, which results in single‐point failure. Sometimes the authentication process is slower, eventually hampers user experience, and thus users are inclined towards other alternatives that can offer multi‐factor authentication. To solve such issues, a secure identity key and blockchain‐based authentication framework is designed using a hybrid blockchain system in multi‐WSN to enhance the system's security. The nodes cooperate in the network to perform specific tasks such that interaction between nodes ensures the legitimacy of node's identity. With hybrid blockchain technology, nodes' identity information is authenticated and stored in the network after verification to establish communication among ordinary nodes and between the ordinary nodes and end user. The proposed method obtains higher security by achieving less computation time, higher detection rate, and limited memory usage of 0.072 s, 92.48%, and 3.925 MB, respectively. The developed authentication method is proved capable of proving resistance in case of different attacks in the network.

Summary When compared to a cloud‐based application, fog computing allows the doctor to make better decisions in an emergency and also helps secure personal data with less latency. Parkinson's disease (PD) is a kind of brain illness, which causes stiffness, shaking, trouble in walking, talking, and so on. There is various detection techniques have been invented by researchers previously, but less accuracy is a major drawback. The main purpose of this research is the effective PD detection by the progression of optimized deep maxout network (DMN), namely, sea lion shuffled shepherd optimization (SLnSSOA)‐based DMN. Here, the developed method involves fog nodes, blockchain (BC), cloud, medical analyzer, and so on. The input data, which comes from the cloud is sent to the preprocessing phase, where data normalization is used to process it. Additionally, SLnSSOA, a combination of the sea lion optimization algorithm (SLnO) and the shuffled shepherd optimization (SSOA), is used to classify diseases using DMN, where the parameters are trained using SLnSSOA. Moreover, this technique provides an improved classification outcome. The developed method attains maximum testing accuracy of 0.908, sensitivity of 0.930, and specificity of 0.875.

Abstract A deep neural network (DNN) is being used in the healthcare industry to improve care delivery at a lower cost and in less time. A DNN is well‐known for its diagnostic applications. However, it is also increasingly being utilized to guide healthcare management decisions. DNNs have been applied to real‐life disease classification problems. The performances of DNN are improved by reducing the training time, performing fast classification, and improving the parameters such as accuracy and error rate, and so forth. We have proposed a hybrid DNN named Strassen's rectilinear fine‐tune bouncing training (SRFBT) Algorithm by combining Strassen's theorem and bouncing training algorithm. The simulation result shows that the SRFBT algorithm outperforms both the DNN, support vector machine, radial basis function network and deep belief network algorithms in terms of network training, testing time and accuracy on various UCI machine learning healthcare datasets. The SRFBT has improved performance with a minimum average training time of 43.6249 and a maximum accuracy of 96.86.

This research paper develops an efficient model, named Taylor-Tunicate Swarm Algorithm-based Generative Adversarial Networks (Taylor-TSA-based GANs) for cancer prediction. The developed Taylor-TSA incorporates the Taylor series with Tunicate Swarm Algorithm (TSA) algorithm. The Yeo–Johnson (YJ) transformation is employed for the data transformation. The feature fusion is evaluated by Deep Stacked Autoencoder (Deep SAE). The fused feature is given as input to the cancer prediction done by GAN trained by Taylor-TSA. The developed model is an effective and efficient use of information with clinical data. The Taylor-TSA-based GAN is analyzed in terms of accuracy, False Positive Rate (FPR), and True Positive Rate (TPR) with the values of 0.9184, 0.1782, and 0.9246.

Smart cities are highly digitalized cities, with a high volume of data stored digitally, as well as a large number of physical objects and Internet of Things (IoT) devices. It is no surprise that security concerns increase as connected IoT devices become more prevalent. There has long been a perception that Denial-of-Service (DoS) attacks give rise to a notable menace to the security of smart city networks. A statistical method based on feature distance maps is suggested in this work for the identification of DoS attacks in smart city networks. This study aims to build a DoS attack detection model that has a low computational complexity and a low rate of false positives (FPs). The performance of the model is arbitrated using smart city network traffic data set. In order to make processing easier, the features are normalized using Min–Max normalization. A subset of features suitable for attack detection can be evaluated with Pearson Correlation Coefficient. A feature distance map method is proposed for extracting correlations between features between records. Manhattan distance measures are used to generate the feature distance maps. The feature distance maps enhance the statistical analysis process. Hellinger distance measure is used to generate the normal traffic profile by calculating the degree of similarity between the feature distance map of each record and the normal traffic profile mean. On the basis of the proposed threshold, the Hellinger distance is used to evaluate whether an unknown traffic data belongs to normal or attack. Compared with Mahalanobis distance-based DoS detection, the Hellinger distance-based attack detection takes less time to execute. Based on the theoretical and experimental results, the proposed DoS attack detection system has a low computational complexity and a low FP rate. Furthermore, the proposed approach outperforms the existing state-of-the-art feature ranking based DoS attack detection systems.

A novel corona virus (COVID-19) has materialized as the respiratory syndrome in recent decades. Chest computed tomography scanning is the significant technology for monitoring and predicting COVID-19. To predict the patients of COVID-19 at early stage poses an open challenge in the research community. Therefore, an effective prediction mechanism named Jaya-tunicate swarm algorithm driven generative adversarial network (Jaya-TSA with GAN) is proposed in this research to find patients of COVID-19 infections. The developed Jaya-TSA is the incorporation of Jaya algorithm with tunicate swarm algorithm (TSA). However, lungs lobs are segmented using Bayesian fuzzy clustering, which effectively find the boundary regions of lung lobes. Based on the extracted features, the process of COVID-19 prediction is accomplished using GAN. The optimal solution is obtained by training GAN using proposed Jaya-TSA with respect to fitness measure. The dimensionality of features is reduced by extracting the optimal features, which enable to increase the speed of training process. Moreover, the developed Jaya-TSA based GAN attained outstanding effectiveness by considering the factors, like, specificity, accuracy, and sensitivity that captured the importance as 0.8857, 0.8727, and 0.85 by varying training data.

Summary Alzheimer's disease (AD) is an advanced neurodegenerative disease of the brain that affects the nerve system of brain. Previously, several feature extraction and classification methods were discussed, but that methods provide high over fitting problem, which leads to minimization of detection accuracy. To overcome these issues, the multi class classification of AD diseases using bag of deep feature reduction technique and twin support vector machine classifier (TSVM) optimized with gradient decent optimizer is proposed in this manuscript for classifying the AD disease as severe AD, mild cognitive impairment, healthy control. At first, the input EEG signals are pre‐processed. To decrease the execution time and processing time with feature size, a bag of deep features reduction technique is used. The reduced feature signals are classified by optimized TSVM. The simulation process is implemented in MATLAB environment. The proposed model achieves higher accuracy 33.84%, 28.93%, 33.03%, 27.93%, higher precision 22.87%, 16.97%, 16.97%, and 36.97%, compared with the existing methods, such as piecewise aggregate approximation support vector machine (MCC‐EEG‐PAA‐SVM), convolutional neural network (MCC‐EEG‐CNN), conformal kernel‐based fuzzy support vector machine (MCC‐EEG‐CKF‐SVM), Pearson correlation coefficient‐based feature selection strategy with linear discriminant analysis classifier (MCC‐EEG‐ PCC‐LDA).

Unmanned Aerial Vehicle (UAV) is adapted as a novel unit in upcoming wireless infrastructures wherein UAVs can play various roles in different applications. The UAV is utilized for navigating commands to attain surveillance. However, routing and localization are the major issues that own higher mobility and unstable links. This paper devises a novel technique for initiating secured communication in a UAV network (UAVN). At first, the simulation of the UAVN is done. After that, the data transmission is carried out amongst nodes using a routing path; hence, an optimal routing path is formed using the newly devised Tunicate Swarm Political Optimization (TSPO) algorithm. The newly devised TSPO algorithm integrates the Tunicate Swarm Algorithm and Political Optimizer. In addition, the data communication is done using an evaluation and monitoring agent. In addition, the malicious detection is done by a decision-making agent using the deep residual network (DRN). Here, the newly devised TSPO algorithm is used to train the deep residual. The input parameters considered in the DRN are round trip time, signal strength, packet delivery, packet size, and the number of incoming packets. Once a DRN classifies the attack and genuine users, the defensive agent is employed for attack mitigation. Thus, the attacks in data are mitigated by the defensive agents. The newly devised TSPO-based DRN obtained enhanced performance with the smallest delay of 0.0014 s, the highest detection rate of 0.9325%, and the highest packet delivery ratio of 11491.2%.

With the global rollout of fifth-generation communication networks, the development on sixth-generation (6G) communication network has begun. The 6G technology is an emerging technology that will meet the ever-increasing demands of evolving industrial services and applications. This technology consists of varied number of heterogeneous resources and communication protocols to guarantee the seamless access to the services. Because of its heterogeneous nature and authorization delegation, security is a major concern in 6G communication environment. The emerging blockchain technology can able to solve the aforementioned privacy and security challenges. The blockchain technique also offers enhanced services like openness, decentralization, immutability, trust free, and so on. Motivated by the aforementioned facts, this article proposes a novel idea of integration of privacy preserving blockchain framework with 6G communication network. We also propose an integrated system for blockchain radio access network as a reliable and stable model for 6G networking based on blockchain technology with improved efficiency and protection. Furthermore, the critical components of blockchain such as smart contract, consensus protocol, mathematical model, secure data sharing, and auditing are clearly defined and experimental results are analyzed.

The present trend of automation anddata interchange in industrial technology is known as Industry 4.0. Industry 4.0 is altering its next generation of distribution networks by making them more responsive and efficient. There are various issues when implementing Internet of Thing (IoT) devices in an Industry 4.0 scenario, mainly due to reduced IoT nodes or devices with insufficient infrastructure to operate security solutions. As a result, securing the environment will require a lightweight and effective security solution. The authors of this study offer a lightweight and flexible authentication method as part of an access control strategy for secure data exchange in-vehicle networks. Lightweight operations, hash functions, XOR operations, and concatenation are all used in the proposed protocol. The security of this approach is also evaluated using AVISPA. The authentication process between IoT networks and resilience to various security threats will be demonstrated. The experimental analysis shows that the computational cost attained is 7.96 s, and the communication cost attained is 834 bits. Similarly, the resource utilization for this computation is 11%, and the average verification time is 7 ms. The experimental analysis demonstrates that the proposed method is efficient with lower computational cost, communication costs, verification cost, and resource utilization than the existing state-of-the-art approaches

Cloud computing services provide a wide range of resource pool for maintaining a large amount of data. Cloud services are commonly used as the private or public data forum based on the demand, and the increase in usage has lead to security concerns. The information in the cloud comes under threat due to hackers, and the most common attack on the cloud data is considered as the Distributed Denial of Service (DDoS) attack. This work has concentrated on detecting the DDoS attack by developing the deep learning-based classifier. The service request from the users is collected and grouped as the log information. From the log file, some important features are selected for the classification using the Bhattacharya distance measure to reduce the training time of the classifier. Here, Taylor-Elephant Herd Optimisation based Deep Belief Network (TEHO-DBN), is developed by modifying the Elephant Herd Optimisation (EHO) with the Taylor series and the algorithm thus developed is adopted to train the Deep Belief Network (DBN) for the DDoS attack detection. From the simulation results, it can be concluded that the proposed TEHO based DBN classifier has improved performance with a maximum accuracy of 0.830.

Intrusion detection is the primary model for giving security to the network. There are numerous issues with conventional intrusion detection models (for example, low detection ability against obscure arranges attack, high false caution rate, and inadequate investigation ability). The real challenging in design IDS with enhanced precision and diminished the training time. This paper come up with hybrid intrusion detection model by incorporating the kernel principal component analysis (KPCA) and binary grey wolf optimization (BGWO) with support vector machine (SVM). The curiosity of the paper is the headway of part parameters of the SVM classifier using the customized parameter assurance procedure. This proposed hybrid method streamlines the discipline factor (C) and kernel parameters (σ) and the size of tube ε of SVM, like this enhancing the precision of the classifier and reduces the testing and training time.