Within the constrained resources, intrusions undermine the availability and reliability of wireless sensor networks and therefore proper and effective detection is necessary. This paper is introducing the intrusion detection system, GWORNN, based on grey-wolf optimization of selecting features and a recurrent network classifier operating in a sequence. We test three population sizes, namely 3, 5 and 7 wolves, with the aim to examine exploration-exploitation trade-off of optimal optimizer. The 7wolf GWORNN achieves accuracy of 96

Large-scale healthcare systems face significant challenges in ensuring security and privacy when sharing vast amounts of data across various e-health entities. Existing studies often struggle with high processing costs, latency, energy consumption, and delayed response times. To address these issues, this research proposes a novel Blockchain-Assisted Improved Puma Edge Computing Network (BA-IPEN) for efficient and secure healthcare data management. The proposed model integrates three key modules: data collection at the IoT layer, data processing at the edge layer, and data storage at the cloud layer. Patient physiological data are gathered from IoT sensors and transmitted to edge devices through remote gateway devices. At the edge layer, a Modified Puma Optimization Algorithm (POA) is employed to maximize resource utilization while minimizing energy consumption and latency. Additionally, edge computing performs preprocessing tasks such as missing data filtering and normalization to extract valuable insights from raw sensor data, thereby enhancing overall performance. For secure data storage, the Blockchain-Assisted TwoFish Algorithm is used. This algorithm encrypts collected data, bolstering security. Blockchain technology ensures tamper-proof records and transparent access restrictions by providing a decentralized and immutable ledger for securely storing healthcare data in the cloud. Extensive experiments demonstrate the effectiveness of the BA-IPEN model, revealing significant reductions in computational cost and latency for cloud-based healthcare data storage. Experimental results also confirm the superiority of the BA-IPEN model over traditional mechanisms, showcasing improvements in performance indicators and reduced energy consumption.

In India, liver disease ranks as the tenth leading cause of death, causing 2.95 percent of all deaths. According to the World Health Organization, liver disease is one of the main causes of death in India. With approximately 10 lakh new cases being diagnosed annually, it has developed into a significant threat in India. One of the most critical parts of automated disease detection and prediction is data mining. Medical data is analyzed using data mining algorithms and methods. Disorders of the liver have increased dramatically in recent years, and liver disease is now one of the leading causes of death in several nations. The patient datasets are looked at so that classification models that can predict liver disease can be made. The aforementioned study used feature implementation and comparative analysis to increase the accuracy rate for liver patients over the course of three phases.On the existing datasets of liver patients derived from the first process's data sets, the min-max normalization algorithm is applied. In the second stage of liver dataset prediction, PSO feature selection is used to extract a subset (data) of liver patient datasets from all normalized liver patient datasets that only contains significant attributes.

Smart devices equipped with embedded systems, including CPUs, sensors, and communication hardware, utilize web connectivity to gather and relay data from their environment, forming a segment of the Internet of Things network. Sensors in an IoT network are resource-constrained devices, but traditional data security techniques use complicated security mechanisms with long processing and reaction times, which reduce the network's overall lifespan. As a consequence, we proposed an EELWEP (energy efficient light weight encryption process) system to keep the data acquired by each sensor node private. When using this strategy, the secret key is exchanged using the Diffie-Hellman method using the Pretty Good Privacy (PGP) program, and a large portion of the operations is carried out via the use of symmetric cryptography. The energy usage and calculation time on the sensor network are significantly reduced as a result of this method. The suggested system is simulated, and their results are analysed using a variety of parameters in comparison to existing benchmark schemes. Comparing the suggested technique to the current approaches reveals that it outperforms the alternatives in the vast majority of situations studied.

In India, agriculture is crucial to the growth of the food industry. Agriculture in our nation is reliant on monsoons, which are an insufficient source of water. Therefore, agriculture uses irrigation. The Internet of Things (IoT) represents a turning point in technological development. IoT is crucial in many industries, including agriculture, which could eventually provide food for billions of people. This study’s objective is to deal with this issue. Since the entire system is microcontroller-based and wireless programmable, there is no need to worry about timing irrigation according to crop or soil conditions. Sensors are used to measure soil properties such as soil moisture, temperature, and air moisture. The user (farmer) controls decision-making by utilizing a microcontroller. The sensor data is transmitted wirelessly to a server-based database. The watering will be automated after the field’s temperature and moisture content have dropped. The condition of the field is periodically updated via mobile notifications to the farmer. This system will be more beneficial in locations with a shortage of water and will be effective in meeting its needs.

With billions of sensors used in numerous IoT applications,IoT is a cutting-edge technology.Sensors gather data for required analysis are crucial components utilized in IoT.This chapter's goal is to cover various healthcare-related applications, technologies, and issues. In order to give healthcare, academia and researchers research directions to address healthcare system difficulties, this chapter reviews, highlights, and identifies the technologies of IoT healthcare systems and can be useful by offering better therapies while effectively utilizing the healthcare.Integration of IoT with smart technologies is discussed in this chapter, which will make the IoT more widespread, profitable, and accessible at all times and locations.Last but not least, several potential possibilities and difficulties are highlighted, along with practical advice that can help the IoT healthcare system.

Cyber-Physical System (CPS) is an emerging technique that focuses on the integration of computation applications as a network of communicating physical and cyber components which controls real time physical substructures of the industrial automation. Since implementation, operation and design of CPS and management of automation substructures are plays major significance in the various industrial applications. This study presents a Deep learning (DL) based intrusion controlling and monitoring management system (DL-ICMMS) for CPS in the automation industry for the detection of the occurrence of intrusions. To transform the input data in a compatible manner, data normalization is carried out and the Adam optimizer is applied to handle hyperparameter values of Restricted Boltzmann Machine (RBM) method. Experimental results analysis stated that the DL-ICMMS method increased accuy of 98.80% whereas the Multilayer Perceptron (MLP), K Nearest Neighbor (KNN), Deep Belief Network (DBN), Convolutional Neural Network (CNN), and Long short-term memory (LSTM) approaches have shown reduced accuy of 93.10%, 93.62%, 91.60%, 90.88%, and 91.75%, correspondingly.

Privacy of data in Internet of Things (IoT) over fog networks is the biggest challenge in security of Wireless communication networks. In Wireless Sensor Network (WSN), current research on fog computing with IoT is gaining popularity among IoT devices over network. Moreover, the data aggregation will reduce the energy consumption in WSN. Due to the open and hostile nature of WSN, secure data aggregation is the major issue. The existing data aggregation methods in IoT and its associated approaches are lack of limited aggregation functions, heavyweight, issues related to the performance overhead. Besides, the overload on fog node will result in high latency, scalability, storage, degraded reliability and energy overhead. In order to overcome these issues, this proposed work has used two schemes for secure transmission of data over the network and reduce the energy consumption of the transmission. The secret data transferred between the IoT devices and the Fog server are transmitted through the aggregator node. If the aggregator node is placed far away from the Fog node, it may send the data to its neighbor aggregator. And it will append it with the current data and send it to the fog server through aggregator message receiving method. In addition to that, the fog server can extract the data through the fog message extractor method. In order to reduce the transmission cost and energy, Clustered Particle Swarm Optimization (CPSO) method is used to form the clusters. This proposed work can avoid the unnecessary energy consumption during the transmission and ensures secured aggregation so that the base station can know the origin of the sender and the validity of the received message. Therefore, the computation cost of the proposed work in authorization requires1MC+1H and the aggregation requires (n+2) MC+1H which is lesser than the existing methods.

Communication of information over the open network is always vulnerable to the data being transferred. Data could be of text, image, video or any other format. Classified data in the form of images, such as, medical record, medical images, insurance policy documents, bank statements, personal identification cards are communicated in these medium. Security to such data is an important concern. Visual secret sharing (VSS) is an encryption scheme to encode classified image data and dividing in to shares. The shares are communicated to the members from source. At the destination, the shares are decoded to reconstruct the classified image data. Individual shares do not disclose the classified information. The shares are communicated to multiple members over the unguarded network. The integrity of the restructured classified image is an important factor to be considered in VSS. In this research paper, a new Cheating prevention by self-authentication (CPS) is proposed to verify the reconstructed image for its integrity. The proposed method ensures no additional share or third party is involved for its verification. Also, the proposed method works on (n,n) VSS scheme and supports color classified image.

Wireless sensor networks have made a significant contribution to wireless sensor communication system based on resource constraints and limited computational sensors. Over the last decade, several focused research efforts have been made to investigate and provide solutions to problems relating to the energy efficiency data fusion aggregation in Wireless sensor networks. However, the problem of designing routes that are energy efficient has not been resolved. It is rather a tough effort to guarantee that the lifespan of a sensor is prolonged for a longer period because of the restricted computational capabilities of sensors, which are often coupled with energy constraints. The findings of this work present an enhanced energy-efficient technique for communication in sensor networks which consists of three distinct innovative frameworks. The suggested framework known as Data Fusion with Potential Energy Efficiency (DFWPEE) is responsible for the optimization of energy. The proposed work reduces energy consumption by using probabilistic methods and clustering. During the data fusion process, the Multiple Zone Data Fusion (MZDF) architecture uses a globular topology that helps with load balancing. The strategy presents an innovative routing approach that is used to aid in the performance of energy efficient routing in large-scale wireless sensor networks. By introducing the idea of routing agents, the framework for the Tree-Based Fusion Technique (TBFT), as suggested, comes up with an innovative method for dynamic reconfiguration. The plan enables the system to determine which sensor has a higher rate of energy dissipation and then immediately transfers the job of data fusion to a node that is more energy efficient. This threshold-based technique enables a sensor to perform both the role of a cluster head and the function of a member node. The node behaves as a cluster head until it achieves its threshold remnant energy and functions as a member node after it passes the threshold residual energy. Both of these roles may be played simultaneously. The mathematical modeling was done using the conventional radio energy model which improved the dependability of attained results. The proposed system delivers enhanced energy efficient communication performance when measured against existing implemented standards for energy efficient schemes. The enhanced technique uses nearly half as much energy as LEACH while focusing on reducing the overall time taken for the process to complete leading to enhanced performance.

The prevalence of Microservices has made it quintessential to build web applications in a Cloud-Native fashion. While building applications in a cloud-native way, almost the entire infrastructure of an organization relies on an arbitrary Cloud Service Provider’s data center as the individual components of the organization’s on-premise infrastructure are morphed into the modern Infrastructure as a Service(IaaS) model in pay-as-you-go strategy. In this scenario, every Cloud Service Provider(CSP) ensures that they are responsible for securing the data at rest. But the data in transit is left to the user’s responsibility. Some prominent Cloud Service Providers offer services to encrypt the data in-transit as well. But under such circumstances, a copy of our enciphering keys are in any way kept under their premises which in turn is undesirable for many individual users and organizations. So, the solution is to do Client Side Encryption(CSE) to ensure the security ourselves. We are proposing a cryptosystem such that it solidifies the integrity of in-transit data by implementing the Homomorphic encryption technique using a modified form of RSA algorithm. (A study on in-flight data security using cloud services is also done.)

In the recent days all the digital devices including devices used in Healthcare, Personal Digital Assistance etc. are connected to the network, consequently data is exposed to various types of attacks. Wide range of attacks has breached the public networks so far and still the number of attacks keeps increasing. Thus, intrusion detection systems (IDS) are developed to monitor and secure the data from such cyber-attacks. Every time the intruders use different approach to hack the networks. In order to effectively secure the networks from cyber-attacks, Machine Learning methodology is being adopted. The network data usually have large number of features and using all the features in identifying the attack will affect the performance of the IDS in terms of accuracy and processing speed. Hence there is a need for an effective IDS which adopts feature selection and classification methodology. In this paper, an enhanced Intrusion Detection scheme is suggested by employing the Greyish Wolf Optimizer (GWO) with LSTM based Recurrent Neural Networks (GWO LSTM). Further, the NSL KDD and UNSW-NB15 datasets are utilized to exhibit the overall performance of suggested scheme and also equate it along with other pre-existing strategies. The results clearly shows that our proposed method adds significant improvement in performance with accuracy of 96.1

Security plays an important part in this Internet world because of the hasty improvement of Internet customers. Different Intrusion Detection Systems (IDS) have been advanced for various departments in history to describe and identify intruders utilizing data processing methods. Nonetheless, when using data processing, existing systems do not achieve adequate detection accuracy. For this reason, we suggest new IDS to offer preservation in statistics communications by completely describing intruders on wireless systems. Here, a new feature selection algorithm called enhanced conditional random field based feature selection to select the most contributed features and optimized hybrid deep neural network (OHDNN) is presented for the classification process. The hybrid deep neural network is a hybridization of convolution neural network (CNN) and long short-term memory (LSTM). To enhance the performance of the HDNN classifier, the parameters are optimized using adaptive golden eagle optimization. The performance of the presented approach is analyzed based on different metrics. For experimental analysis, the NSL-KDD and UNSW-NB15 datasets are used to compare its performance with other popular machine learning algorithms such as ANN, SVM, LSTM and CNN.

In recent years, due to the rapid progress of various technologies, wireless computer networks have developed. However, the activities of the security threats and attackers affect the data communication of these technologies. So, to protect the network against these security threats, an efficient IDS (Intrusion Detection System) is presented in this paper. Namely, optimized long short-term memory (OLSTM) network with a stacked auto-encoder (SAE) network is proposed as an IDS system. Using SAE, significant features are extracted from the databases such as input NSL-KDD database and the UNSW-NB15 database. Then extracted features are given as input to the optimized LSTM which is used as an intrusion identification system. To enhance the effectiveness of the LSTM, we present the pigeon optimization algorithm (POA). Using this algorithm, weight parameters of the LSTM are chosen optimally. Finally, the proposed IDS model decides whether the input packets are intruded or not. The results confirm that the proposed IDS model surpasses the previous machine learning-based IDS models in terms of correctness, F1-score and G mean.

Privacy is the most important part of IOVs since various attacks such as location tracking and stealing sensitive information to create considerable risk for human lives. The attacker changes confidential information such as speed, direction, path, and location of vehicle owner and exploits its privacy. The existing privacy-preserving schemes such as pseudonym schemes, anonymous signing protocol, group signature, and authentication-based schemes, and mix zone and silent period are inefficient in terms of storage, privacy-preserving, and implementation. Furthermore, they impose high overhead and converge very slowly. To remove the issues mentioned above of existing privacy-preserving schemes in IOVS, we suggest a privacy-preservation system to secure the passenger’s privacy, which is based on an ID-based cryptosystem and pseudonym.

Usage of Internet has increased enormously in this decade due to the pandemic began due to COVID19. Data in the form of Text, Image and Video are communicated across the world through mails, chat applications and meeting applications etc. Such third-party applications are vulnerable while sensitive information such as personal, financial, medical and military communications occurs. Visual Cryptography (VC) is an encryption scheme that protects data in the form of image. In source, VC encrypts the image by dividing into shares and distributes to the receiver side. At the destination, the shares are stacked together physically or digitally to reveal the original data. VC also uses cover images to enhance security. VC supports secret sharing of multiple images. In this research paper, a new Multiple grayscale Secret Image Sharing (MSIS) strategy is proposed for secure transmission of more than one grayscale secret image data to the destination. MSIS uses color cover images to enhance the security. It also minimizes the number of shares and cover images to reduce the complexity.

Vehicular ad hoc networks (VANETs) provide real-time communication between vehicle units for comfortable and safe driving. VANETs are driven by the concept of broadcasting messages to other vehicle units. These messages are disseminated to other vehicles with proper security assurances that ensure authentication. In this paper, an authentication model for securing communications by authenticating vehicles in VANETs is developed, and an ECC algorithm is provided for the authentication of vehicles. Since the nodes are mobile in nature and clustered into zones, a connected dominating set-based privacy preservation (CDSPP) and routing algorithm is proposed to effectively route the packets between the authenticated vehicles. A results analysis shows that the proposed method is better than existing approaches in terms of authenticating vehicles and strictly avoids intrusions in the system. A simulation is carried out with NS 2.34 to evaluate the proposed method. The performance of the proposed method is tested with respect to various metrics, including the packet delivery ratio, latency, average routing overhead, and complexity of signature verification. The proposed CDSPP approach is compared with conventional authentication methods, including CPPA and EIBS. The result shows that the proposed CDSPP method achieves an improved packet delivery ratio and reduces the latency, average routing overhead, and signature complexity compared to those of CPPA and EIBS.

Cloud computing models use virtual machine (VM) clusters for protecting resources from failure with backup capability. Cloud user tasks are scheduled by selecting suitable resources for executing the task in the VM cluster. Existing VM clustering processes suffer from issues like preconfiguration, downtime, complex backup process, and disaster management. VM infrastructure provides the high availability resources with dynamic and on-demand configuration. The proposed methodology supports VM clustering process to place and allocate VM based on the requesting task size with bandwidth level to enhance the efficiency and availability. The proposed clustering process is classified as preclustering and postclustering based on the migration. Task and bandwidth classification process classifies tasks with adequate bandwidth for execution in a VM cluster. The mapping of bandwidth to VM is done based on the availability of the VM in the cluster. The VM clustering process uses different performance parameters like lifetime of VM, utilization of VM, bucket size, and task execution time. The main objective of the proposed VM clustering is that it maps the task with suitable VM with bandwidth for achieving high availability and reliability. It reduces task execution and allocated time when compared to existing algorithms.

Protecting Personally identifiable information (PII) and Protected health Information (PHI) is always challenging in this digital era. An ongoing global pandemic of coronavirus disease 2019 called as coronavirus pandemic (COVID-19), has forced people towards digital transaction of data all over the world. Telemedicine allows health care service providers to evaluate, diagnose and treat patients at a distance using telecommunications tools and technology. Patients communicate the medical reports, medical images and related documents by email. Data that carries PHI information, is communicated via public networks in the form of image and are vulnerable. An effective encryption technique is always in need to transfer such information securely. Visual Secret Sharing (VSS) scheme is an efficient encryption scheme that decodes the image by dividing into number of shares. Individual shares do not reveal any secret and stacking of all shares can reveal the secret image. In this article, Semantic Visual Secret Sharing scheme (SVSS) is proposed, which can be applicable for both gray-scale and color images. In this SVSS, the secret color image I is converted into semantic image SI by reducing the pixel errors. This SI decreases the encoding complexity without affecting the quality. The proposed SVSS avoids pixel expansion issues faced by traditional VSS schemes. Also, the Peak Signal-to-Noise Ratio (PSNR) value of the reconstructed secret color image shows better quality of the reconstructed secret image. The pixel errors that get introduced during share generation phase is reduced. The experimental result shows the effectiveness of the proposed SVSS and ensures secure transmission.