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A secure signature‐based access control and key management scheme for fog computing‐based IoT‐enabled big data applications 一种安全的基于签名的访问控制和密钥管理方案,用于基于雾计算的物联网大数据应用
Pub Date : 2023-11-01 DOI: 10.1002/spy2.353
Vijay Karnatak, Amit Kumar Mishra, Neha Tripathi, Mohammad Wazid, Jaskaran Singh, Ashok Kumar Das
Fog computing is a distributed computing architecture, as opposed to depending entirely on centralized cloud servers, which brings the processing of data, functionality of an application, and its storage closer to the network's edge, where it can be closer to the data source or an end‐user device. Some of the potential applications of the fog computing‐based Internet of Things (IoT)‐enabled system are smart healthcare, smart agriculture, smart manufacturing, intelligent transportation system, and smart cities (i.e., in parking management, lighting control, traffic control, and security of civilians). The fog computing‐based IoT‐enabled system is vulnerable to various attacks. Therefore, one needs to deploy security mechanisms, like authentication, access control, key management, and malware detection, in order to secure its communication. In this article, we design a signature‐based access control and key management scheme for fog computing‐based IoT‐enabled big data applications (in short, SBAC‐FC). A detailed security analysis and performance comparison of the SBAC‐FC with other similar existing schemes reveal that the SBAC‐FC surpasses the existing schemes in terms of security and functionality characteristics, as well as complexity overheads.
雾计算是一种分布式计算架构,而不是完全依赖于集中式云服务器,它使数据的处理、应用程序的功能及其存储更接近网络的边缘,在那里它可以更接近数据源或最终用户设备。基于雾计算的物联网(IoT)支持系统的一些潜在应用包括智能医疗、智能农业、智能制造、智能交通系统和智能城市(即停车管理、照明控制、交通控制和平民安全)。基于雾计算的物联网系统很容易受到各种攻击。因此,需要部署安全机制,如身份验证、访问控制、密钥管理和恶意软件检测,以保护其通信。在本文中,我们为基于雾计算的物联网大数据应用(简称SBAC - FC)设计了一种基于签名的访问控制和密钥管理方案。详细的安全性分析和SBAC - FC与其他类似现有方案的性能比较表明,SBAC - FC在安全性和功能特征以及复杂性开销方面优于现有方案。
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引用次数: 0
Personal health record storage and sharing using searchable encryption and blockchain: A comprehensive survey 使用可搜索加密和区块链的个人健康记录存储和共享:一项综合调查
Pub Date : 2023-10-23 DOI: 10.1002/spy2.351
Abhishek Bisht, Ashok Kumar Das, Debasis Giri
Abstract Personal Health Records (PHRs) allow patients to have full control over their health data. However, storage and sharing of PHRs still remains a difficult but necessary task, especially when health data is one of the major targets of cyber attacks worldwide. Searchable Encryption (SE) is a feasible solution for this problem and can be augmented by Blockchain to address some of its issues, such as verifiability. Therefore, SE using blockchain is a promising technologies to tackle the challenge of PHR storage and sharing. In this survey, we have explored the research works that use SE and blockchain technology for the same. The work starts with an introduction of cloud, searchable encryption and blockchain. Subsequently, we present a literature survey of the corresponding technologies. We then describe SE in detail and how it fits with blockchain. This is followed by description of noteworthy existing solutions for secure storage and sharing of PHRs. Even though there have been a number of surveys related to SE, none of them have surveyed the use of blockchain with SE or use of SE and blockchain in PHR sharing. The work concludes with a comparative study of these existing solutions and future scope in this direction.
个人健康记录(PHRs)允许患者完全控制他们的健康数据。然而,存储和共享医疗记录仍然是一项困难但必要的任务,特别是在卫生数据成为全球网络攻击的主要目标之一的情况下。可搜索加密(SE)是解决此问题的可行方案,可以通过区块链增强以解决其一些问题,例如可验证性。因此,使用区块链的SE是解决PHR存储和共享挑战的一种有前途的技术。在本调查中,我们探索了使用SE和区块链技术进行相同研究的研究工作。这项工作从引入云、可搜索加密和区块链开始。随后,我们对相应的技术进行了文献综述。然后,我们详细描述了SE以及它如何与区块链相适应。接下来介绍了用于安全存储和共享phrr的现有解决方案。尽管已经有许多与SE相关的调查,但没有一个调查过区块链与SE的使用情况,也没有调查过在PHR共享中使用SE和区块链的情况。最后,对这些现有的解决方案和这一方向的未来范围进行了比较研究。
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引用次数: 0
An identity‐based secure VANET communication system 基于身份的安全VANET通信系统
Pub Date : 2023-10-21 DOI: 10.1002/spy2.349
Vankamamidi S. Naresh, Sivaranjani Reddi
Abstract Vehicular ad‐hoc networks (VANETs) are mobile networks intended to connect vehicles and provide secure communication. In this direction, many researchers worked on establishing secure communication in VANETs. However, VANETs still face potential security and privacy issues due to network openness. In this paper, we proposed a secure communication system for VANETs with privacy, consisting of an Enhanced privacy‐preserving mutual authentication procedure for safe communication in V2V and deriving a session key using vehicle identities and time stamps the secret values (nonce) shared during the session. Further, we compared the proposed technique with existing techniques, and satisfactory results were obtained in favor of the proposed less computation. Finally, a formal security model is established to secure against unknown key share attacks, replay attacks, and key‐compromised impersonation attacks.
车辆自组织网络(vanet)是旨在连接车辆并提供安全通信的移动网络。在这个方向上,许多研究者致力于在vanet中建立安全通信。然而,由于网络的开放性,VANETs仍然面临着潜在的安全和隐私问题。在本文中,我们提出了一种具有隐私的VANETs安全通信系统,该系统包括一个增强的V2V安全通信的隐私保护相互认证过程,以及使用车辆身份和会话期间共享的秘密值(nonce)时间戳来获得会话密钥。此外,我们将所提出的技术与现有的技术进行了比较,结果表明所提出的计算量更少。最后,建立了一个正式的安全模型,以防止未知密钥共享攻击、重放攻击和密钥泄露冒充攻击。
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引用次数: 0
A secure framework for IoT‐based healthcare using blockchain and IPFS 使用区块链和IPFS的基于物联网的医疗保健安全框架
Pub Date : 2023-10-16 DOI: 10.1002/spy2.348
Deepa Rani, Rajeev Kumar, Naveen Chauhan
Abstract Smart healthcare, also known as IoT (Internet of Things) based healthcare, utilizes IoT technology to enhance the healthcare industry. The use of IoT‐enabled medical equipment enables remote monitoring of patients, allowing for in‐home care and alerting healthcare providers of any changes. This can lead to improved patient outcomes and better disease management. However, it is important to implement robust security measures to protect patient data when using IoT in healthcare. One potential solution is to use blockchain technology to secure data storage and sharing with medical providers. Blockchain's decentralized structure and cryptographic techniques make it difficult for hackers to access or tamper with patient information. Additionally, IPFS (InterPlanetary file system) can be used for efficient data storage and sharing with authorized medical professionals, while smart contract functionality can automate the process of granting and revoking access to patient data. This article proposes a blockchain‐based secure remote patient monitoring system, specifically for chronic disease patients. The system utilizes distributed blockchain for data security, IPFS for data storage and sharing, and DApp for data collection and connection to the blockchain, along with encryption for added security. The proposed approach is compared to existing solutions and found to be a superior option for IoT‐based healthcare.
智能医疗,也被称为基于物联网(IoT)的医疗保健,利用物联网技术增强医疗保健行业。使用支持物联网的医疗设备可以远程监控患者,实现家庭护理,并提醒医疗保健提供者任何变化。这可以改善患者的治疗效果,改善疾病管理。然而,在医疗保健中使用物联网时,实施强大的安全措施来保护患者数据非常重要。一个潜在的解决方案是使用区块链技术来保护数据存储和与医疗提供商共享。b区块链的分散结构和加密技术使黑客难以访问或篡改患者信息。此外,IPFS(星际文件系统)可用于有效的数据存储和与授权的医疗专业人员共享,而智能合约功能可以自动授予和撤销对患者数据的访问权限。本文提出了一种基于区块链的安全远程患者监护系统,专门用于慢性病患者。该系统利用分布式区块链实现数据安全,利用IPFS实现数据存储和共享,利用DApp实现数据收集和连接区块链,并采用加密技术提高安全性。将提出的方法与现有解决方案进行比较,发现这是基于物联网的医疗保健的优越选择。
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引用次数: 0
DeepMDFC: A deep learning based android malware detection and family classification method DeepMDFC:一种基于深度学习的android恶意软件检测和分类方法
Pub Date : 2023-10-16 DOI: 10.1002/spy2.347
Sandeep Sharma, Prachi Ahlawat, Kavita Khanna
Abstract Unprecedented growth and prevalent adoption of the Android Operating System (OS) have triggered a substantial transformation, not only within the smartphone industry but across various categories of intelligent devices. These intelligent devices store a wealth of sensitive data, making them enticing targets for malicious individuals who create harmful Android applications to steal this data for malicious purposes. While numerous Android malware detection methods have been proposed, the exponential growth in sophisticated and malicious Android apps presents an unprecedented challenge to existing detection techniques. Some of the researchers have attempted to classify malicious Android applications into families through static analysis of applications but most of them are evaluated on applications of previous API levels. This paper introduces a novel dataset compromising of 2019 to 2021 applications and proposes a Deep Learning based Malware Detection and Family Classification method (DeepMDFC) to detect and classify emerging malicious Android applications through static analysis and deep Artificial Neural Networks. Experimental findings indicate that DeepMDFC surpasses standard machine learning algorithms, achieving accuracy rates of 99.3% and 96.7% for Android malware detection and classification, respectively, with a limited size feature set. The performance of DeepMDFC is also assessed using the benchmark dataset (DREBIN) and results showed that DeepMDFC surpasses these methods in terms of performance. Furthermore, it leverages the proposed dataset to construct a prediction model that adeptly identifies malicious Android applications from both the years 2022 and 2023. This process the potency and resilience of DeepMDFC against emerging Android applications.
Android操作系统(OS)的空前增长和普遍采用引发了一场实质性的变革,不仅在智能手机行业,而且在各种智能设备领域。这些智能设备存储了大量的敏感数据,使它们成为恶意个人的诱人目标,恶意个人创建有害的Android应用程序来窃取这些数据以达到恶意目的。虽然已经提出了许多Android恶意软件检测方法,但复杂和恶意Android应用程序的指数增长对现有检测技术提出了前所未有的挑战。一些研究人员试图通过对应用程序的静态分析来对恶意Android应用程序进行分类,但大多数恶意Android应用程序都是在以前的API级别上进行评估的。本文介绍了一种新的2019年到2021年应用的数据集,并提出了一种基于深度学习的恶意软件检测和家族分类方法(DeepMDFC),通过静态分析和深度人工神经网络对新兴的恶意Android应用进行检测和分类。实验结果表明,DeepMDFC超越了标准的机器学习算法,在有限大小的特征集下,对Android恶意软件的检测和分类准确率分别达到99.3%和96.7%。DeepMDFC的性能也使用基准数据集(DREBIN)进行了评估,结果表明,DeepMDFC在性能方面优于这些方法。此外,它利用提出的数据集构建一个预测模型,熟练地识别2022年和2023年的恶意Android应用程序。这一过程证明了DeepMDFC对新兴Android应用程序的效力和弹性。
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引用次数: 0
EEG‐based biometric authentication system using convolutional neural network for military applications 基于脑电的卷积神经网络生物识别认证系统在军事上的应用
Pub Date : 2023-10-06 DOI: 10.1002/spy2.345
Himanshu Vadher, Pal Patel, Anuja Nair, Tarjni Vyas, Shivani Desai, Lata Gohil, Sudeep Tanwar, Deepak Garg, Anupam Singh
Abstract In this technological era, as the need for security arises, the use of biometrics is increasing in authentication systems as a secure and convenient method of human identification and verification. Electroencephalogram (EEG) signals have gained significant attention among the various biometric modalities available because of their unique and unforgeable characteristics. In this study, we have proposed an EEG‐based multi‐subject and multi‐task biometric authentication system for the military applications that address the challenges associated with multi‐task variation in EEG signals. The proposed work considers the use of respective EEG signals for the access of artillery, entrance to highly confidential places for the military and so forth by authenticated personnel only. We have used a multi‐subject, multi‐session, and multi‐task () dataset. The dataset was partially preprocessed with basic signal processing techniques such as bad channel repairing, independent component analysis for artifact removal, downsampling to 250 Hz, and an audio filter of 0.01–200 Hz for signal improvisation. This partially preprocessed dataset was further processed and was used in our deep learning model (DL) architectures. For EEG‐based biometric authentication, convolutional neural network (CNN) outperforms many of the state‐of‐the‐art DL architectures with a validation accuracy of approximately 99.86%, training accuracy of 98.49% and precision, recall and F1‐score with values of 99.91% that makes this EEG‐based approach for authentication more reliable. The DL models were also compared based on training and inference time, where CNN used the most training time but took the least time to predict the output. We compared the performance of the CNN model for three preprocessing techniques by feeding mel spectrograms, chromagrams and mel frequency cepstral coefficients, out of which mel spectrograms provided better results. This proposed architecture proves to be robust and efficient for military applications.
在这个技术时代,随着安全需求的增加,生物识别技术作为一种安全便捷的人类身份识别和验证方法在身份认证系统中的应用越来越多。脑电图(EEG)信号由于其独特和不可伪造的特点,在各种可用的生物识别模式中受到了极大的关注。在这项研究中,我们提出了一种基于EEG的多主体多任务生物识别认证系统,用于军事应用,解决与EEG信号多任务变化相关的挑战。拟议的工作考虑使用各自的脑电图信号,以便只有经过认证的人员才能进入大炮、进入高度机密的军事场所等等。我们使用了一个多主题、多会话和多任务的数据集。使用基本的信号处理技术对数据集进行了部分预处理,如坏通道修复、用于去除伪影的独立分量分析、降采样至250 Hz,以及用于信号即兴处理的0.01-200 Hz音频滤波器。这个部分预处理的数据集被进一步处理,并用于我们的深度学习模型(DL)架构。对于基于脑电图的生物识别认证,卷积神经网络(CNN)优于许多最先进的深度学习架构,其验证精度约为99.86%,训练精度为98.49%,精度,召回率和F1得分为99.91%,这使得这种基于脑电图的认证方法更加可靠。还基于训练和推理时间对DL模型进行了比较,其中CNN使用了最多的训练时间,但花费了最少的时间来预测输出。我们通过输入mel谱图、色谱图和mel频率倒谱系数,比较了三种预处理技术下CNN模型的性能,其中mel谱图的效果更好。该体系结构在军事应用中具有鲁棒性和高效性。
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引用次数: 0
Survey on security and privacy in Internet of Things‐based eHealth applications: Challenges, architectures, and future directions 基于物联网的电子健康应用中的安全和隐私调查:挑战、架构和未来方向
Pub Date : 2023-10-04 DOI: 10.1002/spy2.346
Hela Makina, Asma Ben Letaifa, Abderrezak Rachedi
Abstract The integration of Internet of Things (IoT) technology into electronic health (eHealth) applications has revolutionized the healthcare landscape, enabling real‐time patient monitoring, personalized care, and improved patient outcomes. However, this convergence of IoT and healthcare also introduces critical security and privacy challenges, needing careful consideration. This survey comprehensively explores the multifaceted realm of security and privacy issues in IoT‐based eHealth applications. First, we taxonomize the diverse security threats that arise due to the interconnected nature of IoT medical devices. Additionally, we highlight privacy concerns stemming from the collection and sharing of personal health information, while reconciling them with the need for accessible and collaborative healthcare ecosystems. Second, we synthesize functional, ethical, and regulatory perspectives to pick up the major requirements needed in the context of eHealth data during their whole lifecycle, from creation to destruction. Third, we identify emerging research strategies employed to address security and privacy concerns, such as cloud‐based solutions, decentralized technologies such as blockchain technology and InterPlanetary File System (IPFS), cryptographic approaches, fine‐grained access control strategies, and so forth. Additionally, we examine the impact of these approaches on computational efficiency, latency, and energy consumption, critically evaluating their suitability in the healthcare context. Building upon this comprehensive assessment, we outline potential future research directions aimed at advancing security and privacy measures in IoT‐based eHealth applications.
将物联网(IoT)技术集成到电子健康(eHealth)应用程序中,已经彻底改变了医疗保健领域,实现了实时患者监测、个性化护理和改善患者预后。然而,物联网和医疗保健的这种融合也带来了关键的安全和隐私挑战,需要仔细考虑。本调查全面探讨了基于物联网的电子健康应用中安全和隐私问题的多方面领域。首先,我们对由于物联网医疗设备的互联性质而产生的各种安全威胁进行分类。此外,我们强调了收集和共享个人健康信息所产生的隐私问题,同时将其与可访问和协作的医疗保健生态系统的需求相协调。其次,我们综合了功能、伦理和监管方面的观点,从电子健康数据的整个生命周期(从创建到销毁)中挑选出所需的主要要求。第三,我们确定了用于解决安全和隐私问题的新兴研究策略,例如基于云的解决方案、分散技术(如区块链技术和星际文件系统(IPFS))、加密方法、细粒度访问控制策略等。此外,我们还研究了这些方法对计算效率、延迟和能耗的影响,批判性地评估了它们在医疗保健环境中的适用性。在此综合评估的基础上,我们概述了未来潜在的研究方向,旨在推进基于物联网的电子健康应用中的安全和隐私措施。
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引用次数: 0
Nodes selection review for federated learning in the blockchain‐based internet of things 基于区块链的物联网中联合学习的节点选择审查
Pub Date : 2023-09-28 DOI: 10.1002/spy2.344
Mohammed Riyadh Abdmeziem, Hiba Akli, Rima Zourane
Abstract Internet of Things (IoT) gained momentum these last few years pushed by the emergence of fast and reliable communication networks such as 5G and beyond. IoT depends on collecting information from the environment, leading to a significant increase in the amount of data generated that needs to be transmitted, saved, and analyzed. It is clear that classical deterministic approaches might not be suitable to this complex and fast evolving environment. Hence, machine learning techniques with their ability to handle such a dynamic context, are rising in popularity. In particular, Federated Learning architectures which are better suited to the distributed nature of IoT and its privacy concerns. Besides, to address security risks such as model poisoning, device compromise, and network interception, Blockchain (BC) is seen as the secure and distributed underlying communication infrastructure of choice. This integration of IoT, FL, and BC remains in its early stages and several challenges arise. Indeed, nodes selection to perform resource intensive and critical operations like model learning and transactions validation is a crucial issue considering the strong heterogeneity of the involved devices in terms of resources. In this paper, we propose an original literature review including a taxonomy, a thorough analysis, a comparison of the proposed approaches, along with some open research directions.
在5G等快速可靠的通信网络出现的推动下,物联网(IoT)在过去几年中获得了发展势头。物联网依赖于从环境中收集信息,这导致需要传输、保存和分析的生成数据量显着增加。很明显,经典的确定性方法可能不适合这种复杂和快速发展的环境。因此,机器学习技术及其处理这种动态上下文的能力越来越受欢迎。特别是联邦学习架构,它更适合物联网的分布式特性及其隐私问题。此外,为了解决模型中毒、设备泄露和网络拦截等安全风险,区块链(BC)被视为安全、分布式的底层通信基础设施的选择。物联网、FL和BC的整合仍处于早期阶段,并出现了一些挑战。实际上,考虑到所涉及的设备在资源方面具有很强的异质性,选择节点执行资源密集型和关键操作(如模型学习和事务验证)是一个关键问题。在本文中,我们提出了一个原始文献综述,包括分类,深入分析,比较所提出的方法,以及一些开放的研究方向。
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引用次数: 0
A blockchain‐based smart contract model for secured energy trading management in smart microgrids 基于区块链的智能微电网安全能源交易管理智能合约模型
Pub Date : 2023-09-13 DOI: 10.1002/spy2.341
Xiaole Su, Yuanchao Hu, Wei Liu, Zhipeng Jiang, Chan Qiu, Jie Xiong, Ju Sun
Abstract The extension of emerging renewable energy sources such as wind and water turbines, solar panels, and the increasing usage of electric vehicles requires the supply and distribution of energy in a small device on local scale and it has created new methods of supplying and selling electricity. Middle buyers and end users can obtain the local energy with the peer‐to‐peer trading method in this large and hierarchical market. This method enables market to manage and exchange the electricity between major suppliers and medium and local levels. Blockchain technology is developing in peer‐to‐peer exchange of electricity and acts as a reliable, efficient, and safe technology in the electricity trading market. In this method, while preserving the privacy of electricity users, by using smart contracts and by removing intermediaries in the energy supply and demand market, direct commercial interactions between energy suppliers and consumers are done. The blockchain technology, while creating trust between the parties in the energy market, reduces the cost of electricity trading and increases its scalability with using the intermediate energy aggregators. In this research, the blockchain‐based model, is presented for distribution and peer‐to‐peer transactions in the energy market. The suggested model provides the possibility of registration low‐cost instant transactions at the power grid in any specific period of time. The above method, unlike periodic payments, provides immediate access to bills and small payments. Since the transactions outside the blockchain chain are not recorded, this system guarantees its honest and independent operation without fraud and failure. The smart contract method based on blockchain, reduces the transaction fees and speeds up electricity trading. Also, the experimental investigation in 20 nodes shows the time required to determine the exchange contract in the blockchain method. The average is improved by 49.7% in this method. Also, the negotiation convergence time has become 47% faster.
新兴的可再生能源,如风力涡轮机和水轮机、太阳能电池板,以及电动汽车的日益普及,都需要在局部规模的小型设备中供应和分配能源,这创造了新的电力供应和销售方法。在这个庞大的分层市场中,中间买家和最终用户可以通过点对点交易的方式获得本地能源。这种方法使市场能够在主要供应商和中、地方各级之间管理和交换电力。区块链技术在电力点对点交易中不断发展,在电力交易市场中发挥着可靠、高效、安全的作用。在这种方法中,通过使用智能合约和消除能源供需市场中的中介,在保护电力用户隐私的同时,实现了能源供应商和消费者之间的直接商业互动。区块链技术在能源市场各方之间建立信任的同时,降低了电力交易的成本,并通过使用中间能源聚合器提高了其可扩展性。在本研究中,提出了基于区块链的模型,用于能源市场的分配和点对点交易。建议的模型提供了在任何特定时期在电网登记低成本即时交易的可能性。与定期付款不同,上述方法提供了对账单和小额付款的即时访问。由于区块链链外的交易没有记录,因此该系统保证了其诚实独立的运行,没有欺诈和故障。基于区块链的智能合约方法,降低了交易费用,加快了电力交易速度。此外,在20个节点上的实验调查显示了区块链方法确定交换合约所需的时间。该方法平均提高了49.7%。谈判收敛时间也提高了47%。
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引用次数: 1
MetaHate: AI‐based hate speech detection for secured online gaming in metaverse using blockchain MetaHate:基于AI的仇恨言论检测,用于使用区块链在元宇宙中安全的在线游戏
Pub Date : 2023-09-13 DOI: 10.1002/spy2.343
Harshil Sanghvi, Rushir Bhavsar, Vini Hundlani, Lata Gohil, Tarjni Vyas, Anuja Nair, Shivani Desai, Nilesh Kumar Jadav, Sudeep Tanwar, Ravi Sharma, Nagendar Yamsani
The emergence of Web 3.0, blockchain technology (BC), and artificial intelligence (AI) are transforming multiplayer online gaming in the metaverse. This development has its concerns about safety and inclusivity. Hate speech, in particular, poses a significant threat to the harmony of these online communities. Traditional moderation methods struggle to cope with the immense volume of user‐generated content, necessitating innovative solutions. This article proposes a novel framework, MetaHate, that employs AI and BC to detect and combat hate speech in online gaming environments within the metaverse. Various machine learning (ML) models are applied to analyze Hindi–English code mixed datasets, with gradient boosting proving the most effective, achieving 86.01% accuracy. AI algorithms are instrumental in identifying harmful language patterns, while BC technology ensures transparency and user accountability. Moreover, a BC‐based smart contract is proposed to support the moderation of hate speech in the game chat. Integrating AI and BC can significantly enhance the safety and inclusivity of the metaverse, underscoring the importance of these technologies in the ongoing battle against hate speech and in bolstering user engagement. This research emphasizes the potential of AI and BC synergy in creating a safer metaverse, highlighting the need for continuous refinement and deployment of these technologies.
Web 3.0、区块链技术(BC)和人工智能(AI)的出现正在改变虚拟世界中的多人在线游戏。这种发展有其对安全性和包容性的担忧。尤其是仇恨言论,对这些网络社区的和谐构成了重大威胁。传统的审核方法难以应对大量用户生成的内容,因此需要创新的解决方案。本文提出了一个新的框架,MetaHate,它使用AI和BC来检测和打击虚拟世界中在线游戏环境中的仇恨言论。应用各种机器学习(ML)模型分析印地语-英语代码混合数据集,其中梯度增强被证明是最有效的,达到86.01%的准确率。人工智能算法有助于识别有害的语言模式,而BC技术确保了透明度和用户问责制。此外,提出了一个基于BC的智能合约来支持游戏聊天中的仇恨言论的调节。整合人工智能和BC可以显着增强虚拟世界的安全性和包容性,强调这些技术在持续打击仇恨言论和增强用户参与度方面的重要性。这项研究强调了人工智能和BC协同创造更安全的元宇宙的潜力,强调了持续改进和部署这些技术的必要性。
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引用次数: 0
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