Dark Web Data Classification Using Neural Network

There are several issues associated with Dark Web Structural Patterns mining (including many redundant and irrelevant information), which increases the numerous types of cybercrime like illegal trade, forums, terrorist activity, and illegal online shopping. Understanding online criminal behavior is challenging because the data is available in a vast amount. To require an approach for learning the criminal behavior to check the recent request for improving the labeled data as a user profiling, Dark Web Structural Patterns mining in the case of multidimensional data sets gives uncertain results. Uncertain classification results cause a problem of not being able to predict user behavior. Since data of multidimensional nature has feature mixes, it has an adverse influence on classification. The data associated with Dark Web inundation has restricted us from giving the appropriate solution according to the need. In the research design, a Fusion NN (Neural network)-S3VM for Criminal Network activity prediction model is proposed based on the neural network; NN- S3VM can improve the prediction

Smart scalable ML-blockchain framework for large-scale clinical information sharing

Large-scale clinical information sharing (CIS) provides significant advantages for medical treatments, including enhanced service standards and accelerated scheduling of health services. The current CIS suffers many challenges such as data privacy, data integrity, and data availability across multiple healthcare institutions. This study introduces an innovative blockchain-based electronic healthcare system that incorporates synchronous data backup and a highly encrypted data-sharing mechanism. Blockchain technology, which eliminates centralized organizations and reduces the number of fragmented patient files, could make it easier to use machine learning (ML) models for predictive diagnosis and analysis. In turn, it might lead to better medical care. The proposed model achieved an improved patient-centered CIS by personalizing the separation of information with an intelligent ”allowed list“ for clinician data access. This work introduces a hybrid ML-blockchain solution that combines traditional data storage and blockchain-based access. The experimental analysis evaluated the proposed model against the competing models in comparative and quantitative studies in large-scale CIS examples in terms of model viability, stability, protection, and robustness, with improved results

An innovative blockchain-based secured logistics management architecture:utilizing an RSA asymmetric encryption method

Purpose: The recent development in logistics due to the dawn of Logistics 4.0 has made global logistics providers more dependent on intelligent technologies. In this era, these technologies assist in data collection and transmission of logistical data and pose many security and privacy threats in logistics management systems. The customer’s private information, which is shared among the logistics stakeholders for optimal operation, faces unauthorized access due to a lack of privacy. This, amongst others, is a critical problem that needs to be addressed with blockchain. Blockchain is a disruptive technology that is transforming different sectors, and it has the potential to provide a solution to the issues mentioned above, with its unique features such as immutability, transparency, and anonymity. Method: This study designed a blockchain-based logistics management architecture on a decentralized peer-2-peer network using Ethereum smart contracts. The proposed system deployed the Rivest–Shamir–Adleman (RSA) asymmetric encryption method to protect the logistics system from cyber-attacks and secure customers’ private information from unauthorized access. Findings: Furthermore, the security and privacy of the proposed system are evaluated based on the theorem. The proof shows that the system can provide security to the logistics system and privacy to customers’ private data. The performance evaluation is based on throughput and latency. It shows that the proposed system is better than the baseline system, and the comparatives analysis shows that the proposed system is more secure and efficient than the existing systems. Implication and Limitation: The proposed system offers a better solution to the security/privacy of the logistics management system and provides recommendations to key stakeholders involved in the logistics industry while adopting blockchain technology. Apart from the study’s methodological limitation, it is also limited by a lack of reference materials

Cognitive Adaptive Systems for Industrial Internet of Things Using Reinforcement Algorithm

Agile product development cycles and re-configurable Industrial Internet of Things (IIoT) allow more flexible and resilient industrial production systems that can handle a broader range of challenges and improve their productivity. Reinforcement Learning (RL) was shown to be able to support industrial production systems to be flexible and resilient to respond to changes in real time. This study examines the use of RL in a wide range of adaptive cognitive systems with IIoT-edges in manufacturing processes. We propose a cognitive adaptive system using IIoT with RL (CAS-IIoT-RL) and our experimental analysis showed that the proposed model showed improvements with adaptive and dynamic decision controls in challenging industrial environments

Online algorithms for storage utilization under real-time pricing in smart grid

With the rapid proliferation of the advanced metering infrastructure, the smart grid is evolving towards increased customer participation. It is now possible for a utility to influence the customer demand profile via demand side management techniques such as real-time pricing and incentives. Energy storage devices play a critical role in this context, and must be optimally utilized. For instance, the peak power demands can be shaved by charging (discharging) the batteries during periods of low (high) demand. This paper considers the problem of optimal battery usage under real-time and non-stationary prices. The problem is formulated as a finite-horizon optimization problem, and solved via an online stochastic algorithm that is provably near-optimal. The proposed approach gives rise to a class of algorithms that utilize the battery state-of-charge to make usage decisions in real-time. The proposed algorithms are simple to implement, provably convergent for a wide class of non-stationary prices, easy to modify for a variety of use cases, and outperform the state-of-the-art techniques, such as those based on the theory of Markov decision processes or Lyapunov optimization. The robustness and flexibility of the proposed algorithms is tested extensively via numerical studies in MATLAB and real time digital simulator (RTDS)

5G-Enabled Cyber-Physical Systems for Smart Transportation Using Blockchain Technology

The physical world can be controlled directly over the Internet once a Cyber-Physical 1 System (CPS) infrastructure is established. The Intelligent Transportation System (ITS) encompasses Wireless Sensor Network (WSN), Vehicular ad hoc network (VANET), and 5G-enabled Internet of Things (IoT) solutions to transform traditional transportation into an ITS. This research investigates the option of running a blockchain-driven security assurance model to safeguard intelligent roads and smart vehicles as part of ITS. The proposed model considers a semi-distributed model in blockchain deployment to ensure satisfactory Internet of Vehicles (IoV) service while mining acceptable security assurance. The experimental outcomes on intelligent roads and smart parking management indicate that the proposed model achieves comparably good data delivery and reduced latency, paving the way to an innovative deployment of blockchain technologies in IoV for a dependable and trustworthy ITS

Sensors Energy Optimization for Renewable Energy-Based WBANs on Sporadic Elder Movements

The world is advancing to a new era where a new concept is emerging that deals with “wirelessness”. As we know, renewable energy is the future, and this research studied the integration of both fields that results in a futuristic, powerful, and advanced model of wireless body area networks. Every new emerging technology does have some cons; in this case the issue would be the usage of excess energy by the sensors of the model. Our research is focused on solving this excessive usage of energy to promote the optimization of energy. This research work is aimed to design a power-saving protocol (PSP) for wireless body area networks (WBANs) in electronic health monitoring (EHM). Our proposed power-saving protocol (PSP) supports the early detection of suspicious signs or sporadic elder movements. The protocol focuses on solving the excessive energy consumption by the body attached to IoT devices to maximize the power efficiency (EE) of WBAN. In a WSNs network, the number of sensor nodes (SNs) interact with an aggregator and are equipped with energy harvesting capabilities. The energy optimization for the wireless sensor networks is a vital step and the methodology is completely based on renewable energy resources. Our proposed power-saving protocol is based on AI and DNN architectures with a hidden Markov model to obtain the top and bottom limits of the SN sources and a less computationally challenging suboptimal elucidation. The research also addressed many critical technical problems, such as sensor node hardware configuration and energy conservation. The study performed the simulation using the OMNET++ environment and represent through results the source rate to power critical SNs improves WBAN’s scheme performance in terms of power efficiency of Sporadic Elder Movements (SEM) during various daily operations

5G-Enabled Cyber-Physical Systems for Smart Transportation Using Blockchain Technology

The physical world can be controlled directly over the Internet once a Cyber-Physical 1 System (CPS) infrastructure is established. The Intelligent Transportation System (ITS) encompasses Wireless Sensor Network (WSN), Vehicular ad hoc network (VANET), and 5G-enabled Internet of Things (IoT) solutions to transform traditional transportation into an ITS. This research investigates the option of running a blockchain-driven security assurance model to safeguard intelligent roads and smart vehicles as part of ITS. The proposed model considers a semi-distributed model in blockchain deployment to ensure satisfactory Internet of Vehicles (IoV) service while mining acceptable security assurance. The experimental outcomes on intelligent roads and smart parking management indicate that the proposed model achieves comparably good data delivery and reduced latency, paving the way to an innovative deployment of blockchain technologies in IoV for a dependable and trustworthy ITS

An Innovative Blockchain-Based Secured Logistics Management Architecture: Utilizing an RSA Asymmetric Encryption Method

Purpose: The recent development in logistics due to the dawn of Logistics 4.0 has made global logistics providers more dependent on intelligent technologies. In this era, these technologies assist in data collection and transmission of logistical data and pose many security and privacy threats in logistics management systems. The customer’s private information, which is shared among the logistics stakeholders for optimal operation, faces unauthorized access due to a lack of privacy. This, amongst others, is a critical problem that needs to be addressed with blockchain. Blockchain is a disruptive technology that is transforming different sectors, and it has the potential to provide a solution to the issues mentioned above, with its unique features such as immutability, transparency, and anonymity. Method: This study designed a blockchain-based logistics management architecture on a decentralized peer-2-peer network using Ethereum smart contracts. The proposed system deployed the Rivest–Shamir–Adleman (RSA) asymmetric encryption method to protect the logistics system from cyber-attacks and secure customers’ private information from unauthorized access. Findings: Furthermore, the security and privacy of the proposed system are evaluated based on the theorem. The proof shows that the system can provide security to the logistics system and privacy to customers’ private data. The performance evaluation is based on throughput and latency. It shows that the proposed system is better than the baseline system, and the comparatives analysis shows that the proposed system is more secure and efficient than the existing systems. Implication and Limitation: The proposed system offers a better solution to the security/privacy of the logistics management system and provides recommendations to key stakeholders involved in the logistics industry while adopting blockchain technology. Apart from the study’s methodological limitation, it is also limited by a lack of reference materials