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Recently, with the rapid development of information and communication technologies, the infrastructures, resources, end devices, and applications in communications and networking systems are becoming much more complex and heterogeneous. In addition, the large volume of data and massive end devices may bring serious security, privacy, services provi...
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... ML techniques can generally be classified into four different areas, i.e., supervised learning, unsupervised learning, semi-supervised learning, and RL. As depicted in Fig. 6, we present a brief discussion of the four types of ML ...
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... Big data technology serves as a cornerstone for AI, facilitating the collection and analysis of vast datasets generated in smart cities, empowering AI to make informed decisions and predictions. Furthermore, blockchain and deep learning technologies hold promise for enhancing automation services, allowing for autonomous data learning and decision-making (Liu et al., 2020). Authentication technology, viewed through a blockchain lens, is also under scrutiny for smart city applications (Rejeb et al., 2021). ...
A smart city focuses on enhancing and interconnecting facilities and services through digital technology to offer convenient services for both people and businesses. The basic infrastructure of smart cities consists of modern technologies such as the Internet of Things (IoT), cloud computing and artificial intelligence. These urban areas utilize different networks, such as the Internet and IoT, to share real-time information, improving convenience for the inhabitants. However, the reliance of smart cities on modern technologies exposes them to a range of organized, diverse, and sophisticated cyber threats. Therefore, prioritizing cybersecurity awareness and implementing appropriate measures and solutions are essential to protect the privacy and security of citizens. This study aims to identify cyber threats and their impact on smart cities, as well as the methods and measures required for key areas such as smart government, smart healthcare, smart mobility, smart environment, smart economy, smart living, and smart people. Furthermore, this study seeks to evaluate previous research in this field, establish necessary policies to mitigate these threats, and propose an appropriate model for the infrastructure associated with IT networks in smart cities.
... However, the use of machine learning specifically for evaluating and predicting blockchain stability remains underexplored. The potential for machine learning to offer a more adaptive and intelligent approach to stability assessment-by identifying hidden patterns in large, dynamic datasets and developing predictive modelsis promising but still in its nascent stages [6]. ...
Blockchain technology is widely recognized for its security, transparency, and decentralization, yet ensuring the stability of blockchain networks as they scale remains a significant challenge. This study introduces a novel approach by integrating machine learning models to evaluate and predict blockchain stability, offering a proactive solution to maintain network reliability. The primary objective was to identify the key factors influencing stability and assess the effectiveness of different machine learning models in predicting instability events. Using a dataset derived from blockchain transaction data and network metrics, we applied Random Forest, Support Vector Machine (SVM), Long Short-Term Memory (LSTM) neural networks, and K-Means Clustering algorithms. The LSTM model demonstrated the highest accuracy (94.3%) and an AUC-ROC of 0.952, significantly outperforming other models in predicting stability events. The Random Forest model revealed that transaction throughput and network latency are the most critical factors, contributing 35.2% and 28.1% to network stability, respectively. Additionally, K-Means Clustering identified three distinct stability patterns, each representing different risk levels, providing actionable insights for network management. The key contribution of this research lies in the integration of machine learning into blockchain management, presenting a novel approach that enhances the predictability and resilience of blockchain systems. The findings suggest that machine learning can be effectively employed to develop early warning systems, enabling timely interventions to prevent network instability. This study not only advances the understanding of blockchain stability but also offers practical solutions for its enhancement, marking a significant step forward in the field. Future work should focus on the real-time implementation of these models and the exploration of more advanced techniques to further improve predictive capabilities.
... Through a detailed analysis of existing literature, case studies, and experimental results, this paper will highlight the opportunities and limitations of integrating these technologies. Additionally, we will propose strategies for optimizing the use of ML within blockchain environments, with an emphasis on enhancing security, reducing computational overhead, and improving system scalability [3]. ...
This study investigates the integration of machine learning (ML) into blockchain-based smart applications, aiming to enhance security, efficiency, and scalability. The research contributes a novel framework that combines blockchain's decentralized ledger with privacy-preserving ML techniques, addressing key challenges in data integrity and computational efficiency. The primary objective is to evaluate the performance of this integration in a simulated smart grid environment, focusing on security, processing time, energy consumption, and scalability. Our findings reveal that the integrated system significantly improves security, achieving a 98% success rate in mitigating data breaches and reducing the impact of adversarial attacks by 90%. Computational efficiency is also enhanced, with the optimized blockchain-ML configuration reducing processing time by 33% and energy consumption by 20% compared to standard blockchain setups. However, scalability remains a challenge; the system demonstrates effective scalability up to 100 nodes, beyond which transaction processing time increases by 50%, indicating the need for further optimization. The results suggest that while the integration of ML and blockchain offers substantial improvements in security and efficiency, addressing scalability and environmental impact are critical for broader application. The novelty of this research lies in its dual focus on enhancing both security and efficiency within blockchain-ML systems, providing a foundation for future advancements in decentralized intelligent applications across industries. This work contributes to the field by offering empirical data that supports the viability of blockchain-ML integration and by highlighting the areas where further research is needed to realize its full potential.
... Although ML systems can be adapted to process large amounts of data and numerous users, such scaling has some practical limits [34]. With every expansion in the ...
The field of metaverse technology has been relatively growing overall, and the concept of boundaries is now not only from the real world to virtual reality, but now there is an education field that is now one of the driving forces here that is transforming society. The traditional educational models cede to advanced scenarios like e-learning supported by machine-learning systems. This is where educational institutions like Taibah University in Saudi Arabia emerge as leaders in this paradigm change. Taibah University traditionally redefined the study process, which is now digitized, and the geographic borders are being discarded using machine learning in distance learning.
... The anomaly detection model, using complex features, identified five anomalies from a dataset of 1,000 transactions. This demonstrates the effectiveness of the Isolation Forest algorithm in detecting fraud in high-dimensional datasets, validating its use for identifying anomalies in blockchain transactions [16][17][18][19][20]. ...
The study focuses on the enhancement of e-voting blockchain network security through the integration of artificial intelligence. The critical problem addressed is the existing limitations in real-time threat detection and anomaly detection within blockchain transactions. These limitations can compromise the integrity and security of blockchain networks, making them vulnerable to attacks and fraudulent activities. The core results of the research include the development and implementation of sophisticated AI algorithms designed to enhance the monitoring of blockchain transactions and the auditing of smart contracts. These AI-driven advancements introduce unique features, such as the capability to detect and respond to security threats and anomalies in real-time. This significantly strengthens and optimizes the security frameworks of blockchain systems in e-voting. These results are explained by the strategic application of machine learning and natural language processing methodologies. By employing these advanced AI techniques, the study has achieved more accurate and efficient threat detection, thereby addressing the security challenges previously mentioned. The practical applications of these findings are extensive and diverse. Enhanced security mechanisms can be utilized in financial transactions, supply chain management, and decentralized applications, providing a robust framework for improved blockchain-based e-voting security. In conclusion, integrating AI into blockchain security mechanisms addresses current limitations in threat detection and offers a scalable and effective solution for future security challenges
... Blockchain can be used to manage identities, authenticate devices, and secure transactions in a distributed manner. For example, a blockchain-based identity management system can ensure that only authenticated devices are allowed to access network resources, reducing the risk of impersonation attacks [99], [100]. Additionally, blockchain can be used to securely store and share encryption keys, ensuring that they cannot be tampered with or stolen by malicious entities. ...
Ultra Dense Networks (UDNs) have emerged as a pivotal technology in meeting the exponential growth in data demand and providing seamless connectivity in 5G and beyond networks. However, the high density of small cells in UDNs introduces significant challenges related to security, privacy, and performance. This survey paper presents a comprehensive review of the current state-of-the-art in addressing these concerns. It begins by exploring the unique security vulnerabilities inherent to UDNs, including the increased risk of eavesdropping, denial of service attacks, and unauthorized access due to the close proximity of small cells. The paper then discusses privacy issues, particularly the risks of location tracking and user data exposure, exacerbated by the dense deployment of base stations. In terms of performance, the paper evaluates the impact of interference, handover management, and resource allocation on network efficiency. Various proposed solutions, such as advanced encryption techniques, privacy-preserving algorithms, and interference mitigation strategies, are analyzed and compared. The survey concludes by identifying open research challenges and future directions, emphasizing the need for integrated approaches that simultaneously address security, privacy, and performance to ensure the robust operation of UDNs in next-generation wireless networks.
... On the other hand, Blockchain can utilize AI technology to improve data processing capabilities and extend the functionality of smart contracts. A couple of review literatures discussed the combination of Blockchain and AI, and the implications of such integration [114,115,116]. One of the classical review is [114], Khaled Salah et al. presented a detailed survey on the role that Blockchain plays in the context of AI. ...
Supply Chain Finance is very important for supply chain competition, which is an important tool to activate the capital flow in the supply chain. Supply Chain Finance-related research can support multiple applications and services, such as providing accounts receivable financing, enhancing risk management, and optimizing supply chain management. For more than a decade, the development of Blockchain has attracted widely attention in various fields, especially in finance. With the characteristics of data tamper-proof, forgery-proof, cryptography, consensus verification, and decentralization, Blockchain fits well with the realistic needs of Supply Chain Finance, which requires data integrity, authenticity, privacy, and information sharing. Therefore, it is time to summarize the applications of Blockchain technology in the field of Supply Chain Finance. What Blockchain technology brings to Supply Chain Finance is not only to alleviate the problems of information asymmetry, credit disassembly, and financing cost, but also to improve Supply Chain Finance operations through smart contracts to intelligent Supply Chain Finance and in combination with other technologies, such as artificial intelligence, cloud computing, and data mining, jointly. So there has been some work in Blockchain-based Supply Chain Finance research for different Supply Chain Finance oriented applications, but most of these work are at the management level to propose conceptual frameworks or simply use Blockchain without exploiting its deep applications. Moreover, there are few systematic reviews providing a comprehensive summary of current work in the area of Blockchain-based Supply Chain Finance. In this paper, we ...
... Blockchain networks can struggle with scalability [56,57], which can be a significant challenge for machine learning applications. ...
Machine learning-based systems have emerged as the primary means for achieving the highest levels of productivity and efficiency. They have become the most influential competitive factor for many technologies and business companies such as Cloud AI Companies (Google, Facebook…) and Health Care AI Companies (Tempus, Nanox …). However, privacy and security issues have become the biggest challenge facing all ML applications. These threats are known as Adversarial Machine Learning (AML). Their main goal is to maliciously manipulate training data and model parameters to obtain misleading results. To overcome these challenges, several research studies have proven that Blockchain, which relies on cryptographic technologies, constitutes a promising solution for securing ML applications. It provides high guarantees to make the system scalable, reliable and more secure. In this survey, we provide an overview of ML applications and the most hostile attacks they face. Then, we define the blockchain technique and its distinctive features. After that, we conduct a comprehensive and detailed survey of the latest and best blockchain-based research to address security issues in ML applications (integrity, confidentiality and availability). We analyze, compare and comment on all the proposed solutions.
In the end, we discuss in detail the various challenges that hinder the adoption of blockchain technology and we extract the most important scientific trends that will serve as guidance and support for researchers in their future works.
... Not to mention the vast collection of data being stored online pose an additional security threat. Blockchain ensures a significant benefit due to its decentralized, secure, intelligent, and efficient network operation [108]. ...
... Lastly, unmanned aerial vehicles (UAVs) represent a pertinent technology within the domains of geoscience and remote sensing [118]. As seen in figure 7, this is the general architecture of blockchain which consists of 7 layers [108]. The data layer purpose is to store data that was generated during any transaction. ...
The evolution from 5G to 6G signifies a monumental progression in wireless communication technology, promising enhanced capabilities and broader applications. Building on the transformative impact of 5G with its high speeds, low latency, and improved connectivity, the transition to 6G aims to overcome the limitations of its predecessor and unlock new potentials. However, this shift is not devoid of challenges, particularly concerning the privacy and security risks inherent in the adoption of 6G networks. Reflecting on the historical trajectory of wireless technologies, from the first 0G to the current 5G networks, each generational leap has brought significant enhancements in design, coverage, speed, quality of service, capacity, and latency rates. The ongoing deployment of 5G is expected to further expand network capacity through innovative architectural advancements, such as the convergence of information and communication technologies and the implementation of heterogeneous networks. These advancements are essential in optimizing energy consumption, enhancing overall performance, and ensuring the sustainability of wireless networks. Furthermore, the convergence of emerging technologies like the Internet of Things (IoT), energy harvesting, and Simultaneous Wireless Information and Power Transfer (SWIPT) is reshaping the landscape of wireless communication. These technologies not only facilitate the deployment of numerous low-power radios but also pave the way for a more interconnected and efficient wireless ecosystem. In this dynamic world of evolving wireless technologies, the concept of mobile edge computing (MEC) emerges as a novel paradigm for providing computing, storage, and networking resources at the edge of mobile networks. By allowing latency-sensitive and context-aware applications near end-users, MEC ensures efficient operations without compromising performance. This integration of edge computing within the Radio Access Network (RAN) architecture signifies a theoretical shift towards more distributed and responsive network infrastructures. .
... In addition, this decentralized network is designed to fulfill the requisites of data management. Core insights from the latest research indicate that integrating machine learning techniques enhances blockchain applications with robust intelligence for data processing and executing computationally demanding tasks (e.g., Liu et al., 2020;Tian et al., 2021;Dibaei et al., 2022). This appears clearly in the article by Liu et al. (2020), where they noted that combining blockchain with machine learning can process various transactions, allowing for more efficient operations, especially when machine learning is integrated into smart contracts. ...
... Core insights from the latest research indicate that integrating machine learning techniques enhances blockchain applications with robust intelligence for data processing and executing computationally demanding tasks (e.g., Liu et al., 2020;Tian et al., 2021;Dibaei et al., 2022). This appears clearly in the article by Liu et al. (2020), where they noted that combining blockchain with machine learning can process various transactions, allowing for more efficient operations, especially when machine learning is integrated into smart contracts. ...
As emphasized in multiple United Nations (UN) reports, sustainable agriculture, a key goal in the UN Sustainable Development Goals (SDGs), calls for dedicated efforts and innovative solutions. In this study, greenhouse gas (GHG) emissions in the groundnut supply chain from the region of Diourbel & Niakhar, Senegal, to the port of Dakar are investigated. The groundnut supply chain is divided into three steps: cultivation, harvesting, and processing/shipping. This work adheres to UN guidelines, addressing the imperative for sustainable agriculture by applying machine learning-based predictive modeling (MLPMs) utilizing the FAOSTAT and EDGAR databases. Additionally, it provides a novel approach using blockchain-enabled off-chain machine learning through smart contracts built on Hyperledger Fabric to secure GHG emissions storage and machine learning’s predictive analytics from fraud and enhance transparency and data security. This study also develops a decision-making dashboard to provide actionable insights for GHG emissions reduction strategies across the groundnut supply chain.