The Smart Grid aims to enhance the electric grid’s reliability, safety, and efficiency by utilizing digital information and control technologies. Real-time analysis and state estimation methods are crucial for ensuring proper control implementation. However, the reliance of Smart Grid systems on communication networks makes them vulnerable to cyberattacks, posing a significant risk to grid reliability. To mitigate such threats, efficient intrusion detection and prevention systems are essential. This paper proposes a hybrid deep-learning approach to detect distributed denial-of-service attacks on the Smart Grid’s communication infrastructure. Our method combines the convolutional neural network and recurrent gated unit algorithms. Two datasets were employed: The Intrusion Detection System dataset from the Canadian Institute for Cybersecurity and a custom dataset generated using the Omnet++ simulator. We also developed a real-time monitoring Kafka-based dashboard to facilitate attack surveillance and resilience. Experimental and simulation results demonstrate that our proposed approach achieves a high accuracy rate of 99.86%.

During the last decade, large-scale global optimization has been one of the active research fields. Optimization algorithms are affected by the curse of dimensionality associated with this kind of complex problems. To solve this problem, a new memetic framework for solving large-scale global optimization problems is proposed in this paper. In the proposed framework, success history-based differential evolution with linear population size reduction and semi-parameter adaptation (LSHADE-SPA) is used for global exploration, while a modified version of multiple trajectory search is used for local exploitation. The framework introduced in this paper is further enhanced by the concept of divide and conquer, where the dimensions are randomly divided into groups, and each group is solved separately. The proposed framework is evaluated using IEEE CEC2010 and the IEEE CEC2013 benchmarks designed for large-scale global optimization. The comparison results between our framework and other state-of-the-art algorithms indicate that our proposed framework is competitive in solving large-scale global optimization problems.

The rapid growth of the Internet of Things (IoT) and its attributes of constrained devices and a distributed environment make it difficult to manage such a huge and growing network of devices on a global scale. Existing traditional access-control systems provide security and management to the IoT system. However, these mechanisms are based on central authority management, which introduces issues such as a single point of failure, low scalability, and a lack of privacy. In order to address these problems, many researchers have proposed using blockchain technology to achieve decentralized access control. However, such models are still faced with problems such as a lack of scalability and high computational complexity. In this paper, we propose a light-weight hierarchical blockchain-based multi-chaincode access control to protect the security and privacy of IoT systems. A clustering concept with BC managers enables the extended scalability of the proposed system. The architecture of the proposed solution contains three main components: an Edge Blockchain Manager (EBCM), which is responsible for authenticating and authorizing constrained devices locally; an Aggregated Edge Blockchain Manager (AEBCM), which contains various EBCMs to control different clusters and manage ABAC policies, and a Cloud Consortium Blockchain Manager (CCBCM), which ensures that only authorized users access the resources. In our solution, smart contracts are used to self-enforce decentralized AC policies. We implement a proof of concept for our proposed system using the permissioned Hyperledger Fabric. The simulation results and the security analysis show the efficiency and effectiveness of the proposed solution.

The Internet of Things paradigm is growing rapidly. In fact, controlling this massive growth of IoT globally raises new security and privacy issues. The traditional access control mechanisms provide security to IoT systems such as DAC (discretionary access control) and mandatory access control (MAC). However, these mechanisms are based on central authority management, which raises some issues such as absence of scalability, single point of failure, and lack of privacy. Recently, the decentralized and immutable nature of blockchain technology integrated with access control can help to overcome privacy and security issues in the IoT. This paper presents a review of different access control mechanisms in IoT systems. We present a comparison table of reviewed access control mechanisms. The mechanisms’ scalability, distribution, security, user-centric, privacy and policy enforcing are compared. In addition, we provide access control classifications. Finally, we highlight challenges and future research directions in developing decentralized access control mechanisms for IoT systems.

In the Internet of Things (IoT), technological developments have increased the significance of federated cloud systems with integrated cloud providers for exchange transactions. Monolithic IoT systems implement service-oriented architecture (SOA), which is complex for supporting scalability and communicating transactions in a federated cloud system. One weakness of conventional security methods is that they depend on a centralized party, which means there is a single point of failure for the system. In contrast, blockchain (BC) and microservice (MS) technologies allow services to split for independent tasks. In this research paper, we introduce BC security managers based on MS technology for federated cloud systems in an IoT environment. In addition, we present the design of the Federation Security System Manager (FSSM) MS with interoperability features. This enables the exchange of transactions between permissioned BC managers at different cloud providers, with some constraints. Furthermore, a security framework based on MSs and BCs is implemented to ensure security and protect access control. The security functions are deployed based on a smart contract between the permissioned BC managers to achieve interoperability. Finally, we introduce the development process of the proposed framework, which allows for interoperability and ensures the security and privacy of the participating data for a distributed IoT based on the federated cloud system.

Blockchain interoperability has become an essential requirement for the advancement of blockchain technology in numerous fields. Enterprise organizations are increasingly utilizing permissioned blockchains to manage and store their organizations’ data and transactions in a private immutable ledger. Interoperability enables permissioned blockchain platforms to communicate and exchange information which is paramount for fully exploiting permissioned blockchains as facilitators for B2B applications. Additionally, the cross-network invocation of smart contracts under agreed conditions enhances business operations. Blockchain oracles can enable permissioned blockchain interoperability and cross-network transactions in a seamless and private manner. However, they have not been studied in the literature as interoperability techniques between permission blockchains. This study proposes a blockchain oracle interoperability technique designed specifically for permissioned blockchain platforms. We presented the architecture of the blockchain oracle interoperability technique and a prototypical implementation to demonstrate the practicality of the proposed technique. In addition, we obtained cross-network transaction latency measurements and analyzed the results.