SMART : A Secure Remote Sensing Solution for Smart Cities’ Urban Areas

Nowadays, smart cities are becoming an emerging area of research for upgrading and modifying our existing society by adopting the latest and the most trending technologies in the market. Though the number of IoT based applications is constantly increasing, with new products being launched every 6 months, many organizations are afraid of an early adoption of such products because of their security issues. In particular, the transmission and storage of online information causes a lot of cybersecurity issues while ensuring a secure communication mechanism. The aim of this paper is thus to present an efficient and effective communicating mechanism for smart cities using two decision-making models based on the SMART and Subjective approaches. The SMART approach is used to make an intelligent and ideal decision when communicating in the network. In addition, the continuous surveillance of the communicating entities can be done by computing their trust values through a subjective mechanism. The devices having a higher trust value are thus considered as more trustworthy devices. The proposed mechanism is simulated and verified for various security metrics, being compared to the state-of-art approaches. In addition, the proposed mechanism is simulated and out-performed against existing approaches by showing a 97% improvement in terms of accuracy, utility value, delay and threat metrics

A Secure Architectural Model using Blockchain and Estimated Trust Mechanism in Electronic Consumers

Consumer electronics devices, such as refrigerators, washing machines, TVs, smartphones, and household appliances, have become integral to human activities. However, these devices are vulnerable to security breaches and cyber-criminal threats, which can result in the theft and misuse of sensitive information. Existing security surveys and proposed schemes have encountered limitations in terms of redundancy and effectiveness. In this paper, we present a novel approach that ensures secure and transparent communication in consumer electronics. We introduce a multi-criterion decision-making model called TOPSIS, along with a weighted product model, to enhance the security and accuracy of the system. Furthermore, our proposed scheme employs a blockchain system for continuous tracking and monitoring of devices, ensuring accountability and surveillance of their past communications. Through comprehensive validation and verification against existing approaches using various security metrics, our proposed scheme demonstrates superior performance and effectiveness

AI-Based Learning Model for Sociocybernetic Systems in Web of Things : An Efficient and Accurate Decision-Making Procedure

Cybernetic threats have become a growing concern in recent years, highlighting the need for effective intrusion detection systems (IDSs) to detect and prevent social cyberattacks. Sociocybernetics is a significant platform for providing real-time mapping or to enable information access across heterogeneous networks. However, ontology-based knowledge and web support for social cybernetics demand massive warehouses that provide the required computational power for log applications and data-processing mechanisms, in addition to effective decision-support solutions for business by extracting useful information in a very secure and intelligent way. In this work, we propose an IDS approach that combines a tree-based XGBoost algorithm and a bidirectional long short-term memory (BiLSTM) network to address the limitations of traditional approaches. The proposed approach includes multiple steps, such as data preprocessing, feature selection using an infinite feature selection (IFS) algorithm, and the application of principal component analysis (PCA) for dimensionality reduction. Furthermore, a direct trust-based scheme is used to strengthen the decision-making process by improving the overall accuracy in the network. The performance of the proposed approach is evaluated based on accuracy, precision, recall, and F1 score and is compared with the existing LSTM-based deep learning model (LBDMIDS) method. Experimental results demonstrate that the proposed approach outperforms traditional methods by providing higher accuracy along with a slight improvement in terms of precision, recall, and F1 score. In particular, the proposed mechanism shows a 99% improvement in terms of accuracy compared to existing schemes, while also ensuring secure communication in the network

A secure and transparent communication mechanism based on blockchain and fuzzy evaluation matrix in metaverse industry 4.0

Recently, Metaverse is gaining prominence within the field of radiology due to its potential to revolutionize image visualization. Radiologists can harness its capabilities to access dynamic, highly detailed results, thereby enhancing diagnostic precision. Digital twins, at the core of the Metaverse, are digital replicas of real-world objects and entities. They serve as the foundational building blocks, enabling the creation of virtual counterparts for everything within the Metaverse. To ensure the reliability of these digital twins, blockchain technology offers a multi-dimensional data storage solution, reinforcing data integrity and trustworthiness. It is used to ensure a transparent and 3D visualization of each communication and interaction for further looking up any criticality if present in the network. With the rapid increase in value and volume of data, the evolution of the metaverse faces a number of privacy and security concerns. Furthermore, the metaverse in Industry 4.0 is a trending topic that further needs to focus on its security challenges at its initial stage. Fortunately, blockchain is considered as one of the significant solutions. The aim of this paper is to propose a secure and efficient fuzzy evaluation matrix by computing the trust values along with integrating with blockchain mechanism in industry 4.0 enabling metaverse environment. The proposed mechanism is validated against various security concerns such as broken authentication, eavesdropping, personal information leakage, data injection, and unauthorized access. The proposed mechanism showed the validation rate against existing schemes with an improvement of 94% in comparison of several security metrics

ANN Assisted-IoT Enabled COVID-19 Patient Monitoring

COVID-19 is an extremely dangerous disease because of its highly infectious nature. In order to provide a quick and immediate identification of infection, a proper and immediate clinical support is needed. Researchers have proposed various Machine Learning and smart IoT based schemes for categorizing the COVID-19 patients. Artificial Neural Networks (ANN) that are inspired by the biological concept of neurons are generally used in various applications including healthcare systems. The ANN scheme provides a viable solution in the decision making process for managing the healthcare information. This manuscript endeavours to illustrate the applicability and suitability of ANN by categorizing the status of COVID-19 patients' health into infected (IN), uninfected (UI), exposed (EP) and susceptible (ST). In order to do so, Bayesian and back propagation algorithms have been used to generate the results. Further, viterbi algorithm is used to improve the accuracy of the proposed system. The proposed mechanism is validated over various accuracy and classification parameters against conventional Random Tree (RT), Fuzzy C Means (FCM) and REPTree (RPT) methods

A Fast Handoff Technique in Wireless Mesh Network (FHT for WMN)

AbstractIn dynamic nature of WMN, handoff latency is a significant parameter of research. When a mesh client leaves the range of serving mesh router and searches for accessing a new router based on good SNR (Signal to Noise) ratio, a handoff procedure takes place. Whenever a mobile client leaves the range of its Home Mesh Router (HMR) and connects to a Foreign Mesh Router (FMR), mobile (roaming) client needs to authenticate itself as a legitimate node to its FMR in order to get the network services. Several handoff authentication techniques have been suggested by different researchers but leads to certain types of drawbacks i.e. handoff latency, computational overhead, security threats and storage overhead. In order to overwhelm over these hitches, this manuscript propose a technique Fast Handoff Technique (FHT). The suggested technique is compared and evaluated over the network metrics i.e. handoff latency and computational overhead. Further the approach is proved by describing a formal analysis over parameters

Large-scale data streaming, processing, and blockchain security Advances in information security, privacy, and ethics (AISPE) book series./ Hemraj Saini, Geetanjali Rathee, Dinesh Kumar Saini.

"Premier Reference Source" -- taken from front cover.Includes bibliographical references and index."This book explores the latest methodologies, modeling, and simulations for coping with the generation and management of large-scale data in both scientific and individual applications"--Section 1. Chapter 1. A study of big data processing for sentiments analysis ; Chapter 2. An insight on the class imbalance problem and its solutions in big data ; Chapter 3. Large-scale data streaming in fog computing and its applications ; Chapter 4. Trust and reliability management in large-scale cloud computing environments -- Section 2. Chapter 5. Large-scale data storage scheme in blockchain ledger using IPFS and NoSQL ; Chapter 6. Application of deep learning in biological big data analysis ; Chapter 7. Building better India: powered by blockchain ; Chapter 8. Blockchain-based digital rights management techniques ; Chapter 9. Understanding blockchain: case studies in different domains ; Chapter 10. Integrating blockchain and IoT in supply chain management: a framework for transparency and traceability ; Chapter 11. Electronic voting application powered by blockchain technology.1 online resource (xxxi, 285 pages)

An Accurate and Inter-Operatable Fuzzy-Based System Using Genetic and Canonical Correlation Analysis Methods in Internet-of-Brain Things

The brain computer interface is defined as the way of acquiring the brain signals that analyse and translates them into commands that are relayed to intelligent devices for carrying out various actions. Through number of BCI mechanism and approaches have been proposed by various scientists to empower the individuals for directly controlling their objects via their thoughts. However, the actual implementation and realization of this method faces number of challenging with low accuracy and less interoperability. In addition, the pre-processing signals and feature extraction process is further time consuming and less accurate. In order to overcome the mentioned issues, this paper proposes an accurate and highly inter-operable system using genetic fuzzy system along. The predictive model and analysis can be further improved using canonical correlation analysis. The proposed framework is validated and demonstrated using brain typing system analysis. The results are computed against accuracy, latency and interoperability of the signals received from brain with less SNR along with traditional method. The proposed mechanism shows approximately 87% improvement as compare to existing approaches during the simulation over various performance metrics