Bitcoin mining is a wasteful and resource-intensive process. To add a block of transactions to the blockchain, miners spend a considerable amount of energy. The Bitcoin protocol, named 'proof of work' (PoW), resembles a lottery and the underlying computational work is not useful otherwise. In this paper, we describe a novel 'proof of useful work' (PoUW) protocol based on training a machine learning model on the blockchain. Miners get a chance to create new coins after performing honest ML training work. Clients submit tasks and pay all training contributors. This is an extra incentive to participate in the network because the system does not rely only on the lottery procedure. Using our consensus protocol, interested parties can order, complete, and verify useful work in a distributed environment. We outline mechanisms to reward useful work and punish malicious actors. We aim to build better AI systems using the security of the blockchain.
Sun Yat-sen University（中山大学）；Macau University of Science and Technology（澳门科技大学）
Blockchain is gaining extensive attention due to its provision of secure and decentralized resource sharing manner. However, the incumbent blockchain systems also suffer from a number of challenges in operational maintenance, quality assurance of smart contracts and malicious behaviour detection of blockchain data. The recent advances in artificial intelligence bring the opportunities in overcoming the above challenges. The integration of blockchain with artificial intelligence can be beneficial to enhance current blockchain systems. This article presents an introduction of the convergence of …
Blockchain technology and artificial intelligence (AI) are current hot topics in research and practice. However, the potentials of their combination have been studied just recently to a larger extend. While different use cases for combining AI and blockchain have been discussed, the idea of enabling blockchain-based smart contracts to perform "smarter" decisions by using AI or machine learning (ML) models has only been considered on the conceptual level so far. It remained open, how such AI-enabled smart contracts could be implemented in a robust way for real-world applications. Therefore, in …
The emergence of the Internet of Things (IoT) technology has caused a powerful transition in the cyber threat landscape. As a result, organisations have had to find new ways to better manage the risks associated with their infrastructure. In response, a significant amount of research has focused on developing efficient Cyber Threat Intelligence (CTI) sharing platforms. However, most existing solutions are highly centralised and do not provide a way to exchange information in a distributed way. In this chapter, we subsequently seek to evaluate how blockchain technology can be used to address a …
The confluence of Internet of Things(IoT) , Blockchain(BC) and Artificial Intelligence(AI) acts as a key accelerator for enabling Machine Economy. To be ready for future businesses these technologies needs to be adapted by extending the IoT capabilities to Economy of Things (EoT) capabilities. In this paper we focus on one such implementation experience for Smart Toll Transaction application in the domain of mobility. Our paper showcases a possible solution by leveraging negotiations, decision making, distributed learning capabilities at the devices level using AI-enabled Multi-Agent Systems a…
With the advances of the Internet of Things (IoT) and 5G/6G wireless communications, the paradigms of mobile computing have developed dramatically in recent years, from centralized mobile cloud computing to distributed fog computing and mobile edge computing (MEC). MEC pushes compute-intensive assignments to the edge of the network and brings resources as close to the endpoints as possible, addressing the shortcomings of mobile devices with regard to storage space, resource optimisation, computational performance and efficiency. Compared to cloud computing, as the distributed and closer infras…
Cyber Threat Intelligence (CTI) is the knowledge of cyber and physical threats that help mitigate potential cyber attacks. The rapid evolution of the current threat landscape has seen many organisations share CTI to strengthen their security posture for mutual benefit. However, in many cases, CTI data contains attributes (e.g., software versions) that have the potential to leak sensitive information or cause reputational damage to the sharing organisation. While current approaches allow restricting CTI sharing to trusted organisations, they lack solutions where the shared data can be verified …
Cloud computing plays a critical role in modern society and enables a range of applications from infrastructure to social media. Such system must cope with varying load and evolving usage reflecting societies interaction and dependency on automated computing systems whilst satisfying Quality of Service (QoS) guarantees. Enabling these systems are a cohort of conceptual technologies, synthesized to meet demand of evolving computing applications. In order to understand current and future challenges of such system, there is a need to identify key technologies enabling future applications. In this…
In recent years Industrial Control Systems (ICS) have been targeted increasingly by sophisticated cyberattacks. Improving ICS security has drawn significant attention in the literature that emphasises the importance of Cyber Threat Intelligence (CTI) sharing in accelerating detection, mitigation, and prevention of cyberattacks. However, organisations are reluctant to exchange CTI due to fear of exposure, reputational damage, and lack of incentives. Furthermore, there has been limited discussion about the factors influencing participation in sharing CTI about ICS. The existing CTI-sharing platf…
SkyChain Intelligence: A Blockchain-Secured Multi-Agent DRL Framework for Low-Altitude Embodied Artificial Intelligence
WeBank-NTU Joint Research Institute on Fintech, Nanyang Technological University（微众-南洋理工大学金融科技联合研究院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）理学院）；School of Information and Communication Engineering, University of Electronic Science and Technology of China（电子科技大学信息与通信工程学院）；School of Electrical and Computer Engineering, University of Sydney（悉尼大学电气与计算机工程学院）；Graduate School of Information Sciences, Tohoku University（东北大学信息科学研究科）；Department of Electrical and Computer Engineering, Sungkyunkwan University（成均馆大学电气与计算机工程系）
Connecting Large Language Models with Blockchain: Advancing the Evolution of Smart Contracts from Automation to Intelligence
Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, China（中国教育部教育区块链与智能技术重点实验室）；School of Computer Science and Engineering, Guangxi Normal University, China（中国广西师范大学计算机科学与工程学院）
A Sustainable and Reward Incentivized High-Performance Cluster Computing for Artificial Intelligence: A Novel Bayesian-Time-Decay Trust Mechanism in Blockchain
The number of internet-connected devices has been exponentially growing with the massive volume of heterogeneous data generated from various devices, resulting in a highly intertwined cyber-physical system. Currently, the Edge Intelligence System (EIS) concept that leverages the merits of edge computing and Artificial Intelligence (AI) is utilized to provide smart cloud services with powerful computational processing and reduce decision-making delays. Thus, EIS offers a possible solution to realizing future Intelligent Transportation Systems (ITS), especially in a vehicular network framework. …
