False data injection cyber-physical threat is a typical integrity attack in modern smart grids. These days, data analytical methods have been employed to mitigate false data injection attacks (FDIAs), especially when large scale smart grids generate huge amounts of data. In this paper, a novel data analytical method is proposed to detect FDIAs based on data-centric paradigm employing the margin setting algorithm (MSA). The performance of the proposed method is demonstrated through simulation using the six-bus power network in a wide area measurement system environment, as well as experimental data sets. Two FDIA scenarios, playback attack and time attack, are investigated. Experimental results are compared with the support vector machine (SVM) and artificial neural network (ANN). The results indicate that MSA yields better results in terms of detection accuracy than both the SVM and ANN when applied to FDIA detection.

Blockchain is a technology that ensures data security by verifying database of records established in a decentralized and distributed network. Blockchain-based approaches have been applied to secure data in the fields of the Internet of Things, software engineering, healthcare systems, financial services, and smart power grids. However, the security of the blockchain system is still a major concern. We took the initiative to present a systematic study which sheds light on what defensive strategies are used to secure the blockchain system effectively. Specifically, we focus on blockchain data security that aims to mitigate the two data consistency attacks: double-spend attack and selfish mining attack. We employed the systematic approach to analyze a total of 40 selected studies using the proposed taxonomy of defensive strategies: monitoring, alert forwarding, alert broadcasting, inform, detection, and conceptual research design. It presents a comparison framework for existing and future research on blockchain security. Finally, some recommendations are proposed for blockchain researchers and developers.

Despite the lack of blockchain systems being utilized in modern IoT environments, the prevalence of blockchain technology is increasing, due to its high level of security and accountability. The integration of blockchain technology and access control in a decentralized system for smart home networks is a promising solution to this issue. This paper compares the implementation of attribute-based access control (ABAC) with two popular blockchain platforms, Ethereum and Hyperledger Fabric, for a smart home internet of things (IoT) environment. We present a comprehensive summary of access-control and blockchain-access-control methods, to provide the necessary background for this study. Additionally, we present an original ABAC smart contract for Ethereum, and the modification of a pre-existing Hyperledger Fabric ABAC smart contract, for this comparison. Through the simulation of both implementations, the advantages and limitations will be considered, to determine which is better suited for a smart home IoT environment.

Simulating blockchain technology within the IoT has never been as important. Along with this comes the need to find suitable blockchain simulators capable of simulating blockchain networks within an IoT environment. Despite there being a wide variety of blockchain simulators, not all are capable of simulating within an IoT environment and not all are suitable for every IoT environment. This article will review previously published works and present a list of suitable blockchain simulators as well as a few untested simulators that have the potential to simulate blockchain networks within an IoT environment. A total of 18 blockchain simulators are presented and discussed in this paper. In addition, a comprehensive list of the advantages and limitations of each simulator is presented to demonstrate the best situation in which simulators should be used. Finally, recommendations are made on when each simulator should be used and in what situation it should be avoided.

Advanced as they are, DL models in cyber-physical systems remain vulnerable to attacks like the Fast Gradient Sign Method, DeepFool, and Jacobian-Based Saliency Map Attacks, rendering system trustworthiness impeccable in applications with high stakes like power systems. In power grids, DL models such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks are commonly utilized for tasks like state estimation, load forecasting, and fault detection, depending on their ability to learn complex, non-linear patterns in high-dimensional data such as voltage, current, and frequency measurements. Nevertheless, these models are susceptible to adversarial attacks, which could lead to inaccurate predictions and system failure. In this paper, the impact of these attacks on DL models is analyzed by employing the use of defensive countermeasures such as Adversarial Training, Gaussian Augmentation, and Feature Squeezing, to investigate vulnerabilities in industrial control systems with potentially disastrous real-world impacts. Emphasizing the inherent requirement of robust defense, this initiative lays the groundwork for follow-on initiatives to incorporate security and resilience into ML and DL algorithms and ensure mission-critical AI system dependability.