In recent years, blockchain technology has emerged as a promising solution for securing electronic health records (EHRs) while preserving patient privacy. Traditional e-health systems facilitate EHR sharing among healthcare providers but also introduce significant privacy risks, such as unauthorized access and data breaches. Blockchain, when integrated with privacy-preserving techniques, enhances transparency, integrity, and availability in EHR management. Smart contracts further strengthen security by enabling automated authentication and access control. This paper provides a comprehensive survey of blockchain-based access control frameworks in healthcare, categorizing them into permissioned and permissionless approaches. It also explores cryptographic privacy-preserving techniques designed to mitigate privacy risks. Additionally, blockchain platforms and consensus protocols commonly used in these frameworks are analyzed. The methodology follows a structured paper selection process, leading to the final inclusion of 45 research papers focusing on blockchain-based privacy preservation and access control in healthcare. Furthermore, it presents real-world case studies that illustrate the practical implementation of blockchain-based access control in healthcare settings, highlighting their strengths and challenges. Finally, it identifies privacy-related challenges, open research issues, and future directions to guide further research in this evolving domain.

Recently, false data injection attacks (FDIAs) have been identified as a significant category of cyber-attacks targeting smart grids’ state estimation and monitoring systems. These cyber-attacks aim to mislead control system operations by compromising the readings of various smart grid meters. The real-time and precise locational identification of FDIAs is crucial for smart grid security and reliability. This paper proposes a multivariate-based multi-label locational detection (MMLD) mechanism to detect the presence and locations of FDIAs in real-time measurements with precise locational detection accuracy. The proposed architecture is a parallel structure that concatenates Long Short-Term Memory (LSTM) with Temporal Convolutional Neural Network (TCN). The proposed architecture is trained using Keras with Tensorflow libraries, and its performance is verified using an IEEE standard bus system in the MATPOWER package. Extensive testing has shown that the proposed approach effectively improves the presence-detection accuracy for locating stealthy FDIAs in small and large systems under various attack conditions. In addition, this work provides a customized loss function for handling the class imbalance problem. Simulation results reveal that our MMLD technique has a modest advantage in some aspects. First, our mechanism outperforms benchmark models because the problem is formulated as a multivariate-based multi-label classification problem. Second, it needs fewer iterations for training and reaching the optimal model. More specifically, our approach is less complex and more scalable than benchmark algorithms.

SCADA systems contain many important components that communicate with each other through communication protocols designed for SCADA systems. This paper concerns distributed network protocol 3 (DNP3), which is considered a sufficient, trustworthy, and standard protocol for improving communications between multiple vendors. The vulnerabilities of this protocol form a disaster threat over the whole system, so this paper mentions these weakness points of this protocol. Also, the paper mentions the different types of attacks that exploit these vulnerabilities. So, it is necessary for researchers to continuously study mitigating these attacks without affecting the efficiency of the system. This goal is introduced in deep learning model algorithms dependent on neural networks. This paper introduces an ensemble deep learning algorithm (autoencoders) with decision tree (DT) multiple classification and support vector machine (SVM) multiple classification. After that, applying these two classifications models to a dataset to study the efficiency of each model and compares the results between each of them using performance metrics of deep learning algorithms and confusion matrixes which show the accuracy of each classifier.

Software-defined networking grants many advantages for networking via separating the control plane from the data plane. The Controller is the central and the single point of managing the whole network and responsible for decision-making in the SDN controller. However, using a single controller in the SDN model has a problem in reliability, scalability, and security which causes the SDN controller to be a single point of failure. This paper presents a structure which resolved this problem via building high availability controller (HAC) architecture for the control plane. OpenDayLight (ODL) controller was used as a point of study for making the clustering. D-ITG (Distributed Internet Traffic Generator) was used for benchmarking. It can perform and generate traffic at different layers and measure the important parameters that influence the network traffic and performance: Average delay, Average jitter, Average bitrate (Throughput), Packets dropped and others. Finally, we make a full comparison of the effects of these parameters on performance when using a single controller and a multi-controller.