In this paper, a novel multimodal large language model-based fault detection and diagnosis framework that addresses the limitations of traditional fault detection and diagnosis approaches is proposed. The proposed framework leverages the Generative Pre-trained Transformer-4-Preview model to improve its scalability, generalizability, and efficiency in handling complex systems and various fault scenarios. Moreover, synthetic datasets generated via large language models augment the knowledge base and enhance the accuracy of fault detection and diagnosis of imbalanced scenarios. In the framework, a hybrid architecture that integrates online and offline processing, combining real-time data streams with fine-tuned large language models for dynamic, accurate, and context-aware fault detection suited to industrial settings, particularly focusing on security concerns, is introduced. This comprehensive approach aims to address traditional fault detection and diagnosis challenges and advance the field toward more adaptive and efficient fault diagnosis systems. This paper presents a detailed literature review, including a detailed taxonomy of fault detection and diagnosis methods and their applications across various industrial domains. This study discusses case study results and model comparisons, exploring the implications for future developments in industrial fault detection and diagnosis systems within Industry 4.0 technologies.

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%.

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.

Standard databases play essential roles for evaluating and comparing results obtained by different groups of researchers. In this paper, an Arabic Handwritten Text Images Database written by Multiple Writers (AHTID/MW) is introduced. This database can be used for research in the recognition of Arabic handwritten text with open vocabulary, word segmentation and writer identification. The AHTID/MW contains 3710 text lines and 22896 words written by 53 native writers of Arabic. In addition, ground truth annotation is provided for each text image. The database is freely available for worldwide researchers.