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IoT enables the emergence and implementation of smart devices to address real-world problems and challenges. Today we are surrounded by various smart devices, such as smart phones, smart homes, smart cars, smart televisions, and smartwatches that assist us in making our lives easier and smoother. IoT is a conglomeration of multiple technologies at various layers to impart the best of ubiquitous and pervasive computing to deliver several benefits in a variety of application sectors such as medicine, agriculture, and industry. In an IoT setting, blockchain technology is used for addressing security challenges and eradicating third-party participation. Utilizing public networks for storing or transmitting health care images poses risks of eavesdropping, data breaches, and unauthorized access. Before uploading medical data to the decentralized network, encryption is required to prevent unauthorized access. The aim of this study is to integrate technologies to ensure transactions are conducted safely and securely. We proposed an IoT-Blockchain system based on chaos encryption scheme using Tinkerbell mapping to ensure medical data integrity and authenticity. The suggested approach is examined to assess performance parameters such as, key space analysis, key sensitivity analysis, Information Entropy (IE), histogram, correlation of adjacent pixels, Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Structural Similarity Index (SSIM). These findings demonstrated that the suggested method is extremely efficient in avoiding security breaches and guaranteeing information integrity. [ABSTRACT FROM AUTHOR]

The burgeoning practice of unauthorized acquisition and utilization of personal textual data (e.g., social media comments and search histories) by certain entities has become a discernible trend. To uphold data protection regulations such as the Asia–Pacific Privacy Initiative (APPI) and to identify instances of unpermitted exploitation of personal data, we propose a novel and efficient audit framework that helps users conduct cognitive analysis to determine if their textual data was used for data augmentation. and training a discriminative model. In particular, we focus on auditing models that use BERT as the backbone for discriminating text and are at the core of popular online services. We first propose an accumulated discrepancy score, which involves not only the response of the target model to the auditing sample but also the responses between pre-trained and finetuned models, to identify membership. We implement two types of audit methods (i.e., sample-level and user-level) according to our framework and conduct comprehensive experiments on two downstream applications to evaluate the performance. The experimental results demonstrate that our sample-level auditing achieves an AUC of 89.7% and an accuracy of 83%, whereas the user-level method can audit membership with an AUC of 89.7% and an accuracy of 88%. Additionally, we undertake an analysis of how augmentation methods impact auditing performance and expound upon the underlying reasons for these observations. [ABSTRACT FROM AUTHOR]

With the rapid emergence of smart wearable devices and sensor-enabled medical equipment, patient-related Internet of Things (IoT) data must be handled securely in the smart healthcare sector. Electronic Health Records (EHR) provide the digital version of patient health information that needs to be handled securely while meeting access control, immutability and identity management paradigms. Information is sent to the eHealth systems via a dedicated network that leverages several application domains and is built on a centralized architecture. Regrettably, peer-to-peer (P2P) communications performance suffers substantially from computing complexity. Aiming for information security in EHR, Blockchain technology has remarkable solutions that were made possible to minimize third-party dependencies and increase security. Realizing the nature of IoT sensory equipment deployed in eHealth systems, we intended to design a Blockchain-envisioned lightweight authentication scheme using Ethereum Blockchain (BeLAS). The informal and formal security research demonstrates that BeLAS has a promising security level to withstand password guessing, replay, privileged insider, and forgery attacks and is computationally efficient. The decentralized application and interaction with Ethereum Virtual Machine (EVM) were considered to design smart contracts. Our experimental results prove that implementing a Blockchain-based EHR system is secure and efficient by enabling smart contracts to minimize third-party functionalities. The testbed analysis proves the BeLAS achieves minimal GAS per Ether consumption [1 Gas(gwei) = 10 - 9 ETH] for storing the successful EHR transactions. [ABSTRACT FROM AUTHOR]

Summary: Edge computing has the capability to process data closer to its point of origin, leading to the development of critical autonomous infrastructures with frequently communicating peers. The proposed work aims to evaluate the effectiveness of security and privacy mechanisms tailored for distributed systems, particularly focusing on scenarios where the nodes are closed‐circuit television (CCTV) systems. Ensuring public safety, object tracking in surveillance systems is a vital responsibility. The workflow has been specifically crafted and simulated for the purpose of weapon detection within public CCTV systems, utilizing sample edge devices. The system's primary objective is to detect any unauthorized use of weapons in public spaces while concurrently ensuring the integrity of video footage for use in criminal investigations. The outcomes of prior research on distributed machine learning (DML) techniques are compared with modified federated machine learning (FML) techniques, specifically designed for being Gossip verifiable and Quantum Safe. The conventional federated averaging algorithm is modified by incorporating the secret sharing principle, coupled with code‐based McEliece cryptosystem. This adaptation is designed to fortify the system against quantum threats. The Gossip data dissemination protocol, executed via custom blockchain atop the distributed network, serves to authenticate and validate the learning model propagated among the peers in the network. It provides additional layer of integrity to the system. Potential threats to the proposed model are analyzed and the efficiency of the work is assessed using formal proofs. The outcomes of the proposed work demonstrate that the trustworthiness and consistency are meticulously preserved for both the model and data within the DML framework on the Edge computing platform. [ABSTRACT FROM AUTHOR]

The wearable tech revolution and the proliferation of Internet of Things (IoT) gadgets have created exciting new opportunities for remote patient monitoring. Healthcare providers are increasingly utilizing wearable technologies to expedite the process of diagnosis and treatment. The healthcare and research fields have been substantially impacted by emerging technologies. On the other hand, there are legitimate worries regarding the security of data transfers and transaction recording upon using these technologies. Healthcare departments need easy data interchange for interoperability. Protecting data security and integrity is crucial when exchanging information with authorized parties. In many existing solutions, patients' sensitive data is collected and stored in smart healthcare systems. Scalability, breach or unauthorized access to this data can compromise privacy. The adoption of blockchain technology is one way to safeguard patient privacy in healthcare. Also, by facilitating the safe and secure exchange of data, blockchain technology is revolutionizing the healthcare industry by making traditional methods of diagnosis and treatment more reliable. However, blockchain has serious problems with its highly limited scalability. This article suggests a new method "SHORTBLOCKS" depending on blockchain technology for the safe administration and analysis of data. To circumvent the security-scalability issue and achieve high throughput, this study employs the newly introduced protocol "SHORTBLOCKS", which extends blockchain concept to a direct acyclic graph of blocks. The proposed system uses both a private and a public blockchain that are created on the new protocol. In order to analyze patient health data, a system using smart contracts and a private blockchain is built. The system logs the occurrence to the public blockchain in the case that the smart contract raises an alarm due to an unusual reading. This will fix the scalability issue with initial blockchain as well as the security and privacy issues involved in remote patient monitoring. Simulation results shows the performance of the proposed system by comparing with existing solutions, SPECTRE protocol and GHOSTDAG protocol. [ABSTRACT FROM AUTHOR]

IoT enables the emergence and implementation of smart devices to address real-world problems and challenges. Today we are surrounded by various smart devices, such as smart phones, smart homes, smart cars, smart televisions, and smartwatches that assist us in making our lives easier and smoother. IoT is a conglomeration of multiple technologies at various layers to impart the best of ubiquitous and pervasive computing to deliver several benefits in a variety of application sectors such as medicine, agriculture, and industry. In an IoT setting, blockchain technology is used for addressing security challenges and eradicating third-party participation. Utilizing public networks for storing or transmitting health care images poses risks of eavesdropping, data breaches, and unauthorized access. Before uploading medical data to the decentralized network, encryption is required to prevent unauthorized access. The aim of this study is to integrate technologies to ensure transactions are conducted safely and securely. We proposed an IoT-Blockchain system based on chaos encryption scheme using Tinkerbell mapping to ensure medical data integrity and authenticity. The suggested approach is examined to assess performance parameters such as, key space analysis, key sensitivity analysis, Information Entropy (IE), histogram, correlation of adjacent pixels, Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Structural Similarity Index (SSIM). These findings demonstrated that the suggested method is extremely efficient in avoiding security breaches and guaranteeing information integrity.

Internet of Medical Things (IoMT) applications, called also Medical Internet of Things (MIoT) or IoT for e-health, allow integrating smart technologies to medical devices for a better monitoring of disease progression and patients tracking. While it offers extensive benefits and despite of its expansion, IoMT brings round additional security concerns. Connected Medical Devices (CMD) raise up several security and privacy problems. The complexity and heterogeneity of data and technology in IoMT communications create additional security risks and threats. Malicious users may exploit crucial vulnerabilities in a wide range of IoMT applications, networks and devices. Hence, thinking about smart and efficient security solutions is an urgent need to understand and assess IoMT-related threats. Existing traditional models are no longer convenient and remain unsuitable to address the various born risks. We present in the current manuscript an in depth study of security concerns within IoMT. We review popular risk assessment and management approaches and discuss their suitability to the IoMT context. The main shortcomings are inherent to the complex architecture, the lack of automation and the numerous stakeholders with different security needs and skills.With reference to the conducted study, we introduce our solution as a framework to enhance trustworthiness and support decision making within IoMT environments. The proposal relies on a fine-grained approach for managing associated risks with regard to different areas of focus and common risk factors. To do so, a Machine Learning (ML)-based anomaly detection model and a hybrid Risk Assessment (RA) model are defined to evaluate cumulative IoMT risk. Experiments show obtained competitive results compared to state of the art ML models to detect intrusions in IoT/IoMT systems and we obtained an accuracy rate of 100% with some algorithms. A use case of application is presented to highlight the efficiency of the proposal. [ABSTRACT FROM AUTHOR]

Abstract The timely identification of autism spectrum disorder (ASD) in children is imperative to prevent potential challenges as they grow. When sharing data related to autism for an accurate diagnosis, safeguarding its security and privacy is a paramount concern to fend off unauthorized access, modification, or theft during transmission. Researchers have devised diverse security and privacy models or frameworks, most of which often leverage proprietary algorithms or adapt existing ones to address data leakage. However, conventional anonymization methods, although effective in the sanitization process, proved inadequate for the restoration process. Furthermore, despite numerous scholarly contributions aimed at refining the restoration process, the accuracy of restoration remains notably deficient. Based on the problems identified above, this paper presents a novel approach to data restoration for sanitized sensitive autism datasets with improved performance. In the prior study, we constructed an optimal key for the sanitization process utilizing the proposed Enhanced Combined PSO-GWO framework. This key was implemented to conceal sensitive autism data in the database, thus avoiding information leakage. In this research, the same key was employed during the data restoration process to enhance the accuracy of the original data recovery. Therefore, the study enhanced the restoration process for ASD data's security and privacy by utilizing an optimal key produced via the Enhanced Combined PSO-GWO framework. When compared to existing meta-heuristic algorithms, the simulation results from the autism data restoration experiments demonstrated highly competitive accuracies with 99.90%, 99.60%, 99.50%, 99.25%, and 99.70%, respectively. Among the four types of datasets used, this method outperforms other existing methods on the 30-month autism children dataset, mostly.

Blockchain technology is becoming more prominent and is being used in many different industries. Data islands have emerged as a result of the difficulty in transferring assets and exchanging information between blockchains because of differences in the underlying technology. Cross-chain technology is becoming increasingly prevalent as a solution to the data security problem. Decentralized blockchain networks frequently use the Hashed Timelock Contract (HTLC) to solve the problem of balancing atomicity and time sensitivity. However, it suffers from drawbacks such as limited security and privacy protection capabilities. To overcome these limitations, a secure and fully functional system named the Exchange Smart Contract (ExchangeSC) has been developed; the ExchangeSC can integrate smart contracts and Paillier homomorphic encryption into the Mid-Account HTLC (MA-HTLC) cross-chain protocol. This integration effectively resolves the problem of low security and privacy protection in the HTLC cross-chain protocol. Specifically, the locked information in the solution is encrypted using homomorphic encryption before uploading to the blockchain, which is operated by participating nodes in the ciphertext domain. The ExchangeSC demonstrates reasonable performance on the official testing network's EVM platform. Further evaluation of the ExchangeSC-based HTLC cross-chain reveals its superior security and lower time cost compared to the BitXHub cross-chain project. [ABSTRACT FROM AUTHOR]

Phishing is one of the most important security threats in modern information systems causing different levels of damages to end-users and service providers such as financial and reputational losses. State-of-the-art anti-phishing research is highly fragmented and monolithic and does not address the problem from a pervasive computing perspective. In this survey, we aim to contribute to the existing literature by providing a systematic review of existing experimental phishing research that employs EEG and eye-tracking methods within multi-modal and multi-sensory interaction environments. The main research objective of this review is to examine articles that contain results of at least one EEG-based and/or eye-tracking-based experimental setup within a phishing context. The database search with specific search criteria yielded 651 articles from which, after the identification and the screening process, 42 articles were examined as per the execution of experiments using EEG or eye-tracking technologies in the context of phishing, resulting to a total of 18 distinct papers that were included in the analysis. This survey is approaching the subject across the following pillars: a) the experimental design practices with an emphasis on the applied EEG and eye-tracking acquisition protocols, b) the artificial intelligence and signal preprocessing techniques that were applied in those experiments, and finally, c) the phishing attack types examined. We also provide a roadmap for future research in the field by suggesting ideas on how to combine state-of-the-art gaze-based mechanisms with EEG technologies for advancing phishing research. This leads to a discussion on the best practices for designing EEG and gaze-based frameworks. [ABSTRACT FROM AUTHOR]

Vehicular Ad hoc Networks (VANETs) play a significant role in human life, where the vehicle user connects their vehicles with VANETs and gains several benefits from them, such as traffic-related information, congestion, accidents, etc. Apart from several advantages of VANETs, it comes with several privacy issues. For instance, many malicious vehicles can forward illegitimate information over the VANETs. To overcome this problem, several authentication schemes have been proposed, such as traditional digital signatures, ring signatures, identity-based signatures, group signatures, etc. The problem with these schemes is that it has only one signer. If this signer becomes malicious, it may circulate false information over the VANETs, which makes VANETs untrusted; hence, vehicle users hesitate to use the network. To overcome this problem, this work proposes a threshold signature scheme using the Schnorr signature scheme. This scheme creates a signature using a set of signers and then circulates information over the VANETs. The security of this scheme depends on the elliptic curve discrete logarithm problem. Implementation details and results show that the scheme requires only a few cryptographic operations and completes all the tasks, such as signature generation and verification, within 1.6 milliseconds, even at the security parameter 2024. The scheme performance was also compared with the existing scheme, and it performed around 1.8 times better than the existing schemes, ensuring the proposed scheme is a viable option for VANETs. [ABSTRACT FROM AUTHOR]

The literature has extensively explored vehicle ad hoc networks (VANETs) and vehicular clouds, with a common assumption in these studies being incorporating onboard units (OBUs) in vehicles. OBUs are used to collect and disseminate information between vehicles. Furthermore, numerous studies assume the presence of road infrastructure for communication. Implementing a vehicular cloud can play a vital role in aggregating data on events such as weather conditions, traffic information and accidents. This information is distributed to other vehicles, allowing drivers to make informed decisions and ensure safe driving practices. To protect privacy within the vehicular cloud, pseudonyms serve as a means of communication between vehicles and roadside units (RSU). Numerous existing approaches suggest more frequent updates to vehicle pseudonyms to reduce the likelihood of linking transmitted messages by vehicles. However, some of these strategies overlook situations where vehicle density is low, and vehicles have limited willingness to engage in the pseudonym-changing process. This article introduces an architecture that encourages vehicles to participate in the pseudonym-changing process to enhance vehicle privacy. This is achieved by issuing rewards to vehicles that can be used to access cloud services. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) represents a network framework comprising identifiable entities that interact through the Internet. One of its applications is the smart home, where household devices can be remotely monitored and controlled. This has led to an increased demand for reliable security solutions in IoT systems. Security presents a significant challenge in IoT smart home devices and must be carefully considered. Unauthorized access to a smart home system, facilitated by means such as jamming or replay attacks, could pose risks by manipulating sensors and controls, potentially allowing unauthorized entry. This review paper concentrates specifically on the security and privacy aspects of IoT smart home access control devices. It begins with a concise overview of smart home security and privacy, then delves into various techniques within the smart home system taxonomy, such as authentication, access control, blockchain, and cryptography-based methods. Furthermore, the paper compares the advantages and disadvantages of these techniques. It also examines various types of attacks on smart home IoT access control systems and evaluates risk factors such as methodologies, attack frequency, severity, probability, and ranking. Finally, the paper discusses challenges, applications, conclusions, and future directions. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) devices have become increasingly important within the smart home domain, making the security of the devices a critical aspect. The majority of IoT devices are black-box systems running closed and pre-installed firmware. This raises concerns about the trustworthiness of these devices, especially considering that some of them are shipped with a microphone or a camera. Remote attestation aims at validating the trustworthiness of these devices by verifying the integrity of the software. However, users cannot validate whether the attestation has actually taken place and has not been manipulated by an attacker, raising the need for HCI research on trust and understandability. We conducted a qualitative study with 35 participants, investigating trust in the attestation process and whether this trust can be improved by additional explanations in the application. We developed an application that allows users to attest a smart speaker using their smartphone over an audio channel to identify the attested device and observe the attestation process. In order to observe the differences between the applications with and without explanations, we performed A/B testing. We discovered that trust increases when additional explanations of the technical process are provided, improving the understanding of the attestation process. [ABSTRACT FROM AUTHOR]

Summary: The present scenario witnesses "the second quantum revolution," which has enabled the development of revolutionary novel quantum tools. Quantum computing endeavors to establish higher computing standards that can potentially solve complex structures. Post‐quantum cryptography (PQC) has emerged as one of the new domains of cryptography, which is resilient to quantum attacks owing to the revolution in quantum computing. To ensure quantum security, the lattice‐based cryptosystem (LB‐cryptosystem) is one of the promising tools of PQC to address quantum‐based threats. The traditional security algorithms, such as RSA and Diffie–Hellman (DH), are strong enough to resist present security threats. However, it has been predicted that quantum technologies have the capability to break the security of most traditional algorithms whose security is based on prime factorization and DH‐type hard problems. Therefore, research is currently focused on addressing the security and privacy threats by using LB‐cryptosystems to secure various applications, organizations' data, and information infrastructure in the quantum era. The purpose of this article is to investigate recent advances in LB‐cryptosystems that may allow the design of secure models for smart‐grid networks (SGNs) against existing and future quantum attacks. SGNs have been explored as the bi‐directional assimilation of communication in terms of electricity generation, transmission, allocation, and utilization. This survey provides a comprehensive overview of LB‐cryptosystems, as well as their potential applications in securing SGNs. Lastly, the article summarizes the various PQC primitives, NIST selected algorithms, open‐source tools along with their packages, and various PQC industrial initiatives, and also compares traditional cryptographic schemes with other PQC. [ABSTRACT FROM AUTHOR]

The Internet of Things and blockchain, as important technology foundations to enable Web 3.0, have greatly impacted the living and production patterns of people. When they meet together, blockchain services can be used to solve some IoT challenges that traditional information technologies cannot solve. Therefore, how to use blockchain services in IoT environment, and what key security challenges in IoT environment can be addressed by blockchain services are two critical issues. In this article, we propose a novel hybrid IoT architecture based on blockchain services to solve the first issue. To solve the second issue, we analyze IoT security threats and challenges, and propose blockchain-based security solutions, which are illustrated with the application cases of edge computing and fog computing based on smart contracts. Then we design and implement blockchain-based IoT zero-knowledge proof authentication experiments using the web3.js library to demonstrate the lightweight blockchain services performance in IoT application scenarios. [ABSTRACT FROM AUTHOR]

Space sharing services like vacation rentals and meeting rooms are being equipped with smart devices such as cameras, door locks and many other sensors. However, the sharing of such devices poses privacy and security problems, as there is typically no clear control transfer between owners and users. In this article, we propose Spacelord, a system to time-share smart devices contained in a shared space privately and securely while allowing users to configure them. When a user stays at a space, Spacelord ensures that the smart devices contained in it run code and configurations the user trusts while removing pre-installed code and configurations. When the user leaves the space, Spacelord reverts any changes the user has introduced to the smart devices, deletes any remaining private data and lets the owner take back control over the devices. We evaluate Spacelord for two realistic space-sharing cases—smart home and coworking meeting room—and observe smart space provisioning delays of ~82sacross three different platforms. Moreover, the average runtime overhead of our system varies from 7.8% to 11.8% across different hub hardware, running native applications. [ABSTRACT FROM AUTHOR]

Maritime transportation is crucial for global trade and responsible for the majority of goods movement worldwide. The optimization of maritime operations is challenged by the complexity and heterogeneity of maritime nodes. This paper presents the emerging deployment of federated learning (FL) in maritime environments to address these challenges. FL enables decentralized machine learning model training, ensuring data privacy and security while overcoming issues associated with non-i.i.d. data. This paper explores various maritime use cases, including fuel consumption reduction, predictive maintenance, and just-in-time arrival. Experimental results using real datasets demonstrate the superiority of FL in predicting the fuel consumption of large cargo ships in terms of accuracy and spatiotemporal complexity over traditional collaborative machine learning approaches. The findings indicate that FL can significantly improve the performance of fuel consumption models in a collaborative way, while ensuring data privacy preservation and no data transmission during the learning process. Finally, this paper discusses open issues and future research directions necessary for the widespread adoption of FL in maritime transportation and settings. [ABSTRACT FROM AUTHOR]

Large Language Models (LLMs), such as ChatGPT, encounter 'jailbreak' challenges, wherein safeguards are circumvented to generate ethically harmful prompts. This study introduces a straightforward black-box method for efficiently crafting jailbreak prompts that bypass LLM defenses. Our technique iteratively transforms harmful prompts into benign expressions directly utilizing the target LLM, predicated on the hypothesis that LLMs can autonomously generate expressions that evade safeguards. Through experiments conducted with ChatGPT (GPT-3.5 and GPT-4) and Gemini-Pro, our method consistently achieved an attack success rate exceeding 80% within an average of five iterations for forbidden questions and proved to be robust against model updates. The jailbreak prompts generated were not only naturally worded and succinct, but also challenging to defend against. These findings suggest that the creation of effective jailbreak prompts is less complex than previously believed, underscoring the heightened risk posed by black-box jailbreak attacks. [ABSTRACT FROM AUTHOR]

The paper is an attempt to study the individual's intention to use mobile banking. In light of the results obtained from the study, the proposed model offers a better fit with the data and explains the intention of individuals to use mobile banking services. Government support, trust, and compatibility significantly contribute to the Perceived behavioral control of a bank customer to use mobile banking while Perceived ease of use, Perceived usefulness, Security and privacy, and risk have a significant positive impact on the attitude of the individuals to utilize mobile banking service. The study uses primary data and the final instrument was administered to 950 respondents, across the country of which 904 data were used for the analysis after editing to accommodate the missing values. The study has adopted structural equation modeling approach to analyze the relationships between the variables in the study. The proposed framework in this study can be utilized to identify the factors that promote the adoption of mobile banking practices and the study also has the potential to provide updated and comprehensive literature on mobile banking, which can accelerate future research in this field. [ABSTRACT FROM AUTHOR]

The term "Internet of things (IoT)" refers to a network in which data from all connected devices may be gathered, analyzed, and modified as per requirements to offer new services. IoT devices require a constant Internet connection to exchange data. The volume and speed of data continue to grow quickly with the expansion of IoT devices nowadays. IoT systems frequently use messaging protocols to exchange IoT data. IoT security must be established using advanced techniques as it is vulnerable to many threats. The primary objectives of IoT security are to protect customer privacy, data integrity, and confidentiality, as well as the security of assets and IoT devices and the accessibility of services provided by an IoT ecosystem. In this regard, the IoT must meet user demands while consuming the least number of resources, including money, vitality, and time. The proposed research work is organized into numerous categories to make it easier for researchers and readers to solve and understand security problems in IOT devices. The categories "Features" are identified from available literature, and a specific criterion is adopted for choosing alternatives. The entropy approach to determine criterion relevance by calculating features weights is utilized. The second method "EDAS" approach is used and the alternatives are sorted chronologically based on the criterion weights for easy identification and selection of an effective alternative. Finally, all alternatives are precisely analyzed and ranked. Using our research method, various appropriate features are extracted and are evaluated to solve security issue within IoT devices. The most significant features are ranked to help researchers and manufacturers to focus on security-related issues in IoT devices. [ABSTRACT FROM AUTHOR]

This research explores the user perceptions of the Metaverse Marketplace, analyzing a substantial dataset of over 860,000 Twitter posts through sentiment analysis and topic modeling techniques. The study aims to uncover the driving factors behind user engagement and sentiment in this novel digital trading space. Key findings highlight a predominantly positive user sentiment, with significant enthusiasm for the marketplace's revenue generation and entertainment potential, particularly within the gaming sector. Users express appreciation for the innovative opportunities the Metaverse Marketplace offers for artists, designers, and traders in handling and trading digital assets. This positive outlook is tempered by notable concerns regarding security and privacy within the Metaverse, pointing to a critical area for development and assurance. The study also reveals a substantial neutral sentiment, reflecting users’ cautious but interested stance, particularly regarding the marketplace's role in investment and passive income opportunities. This balanced view underscores the evolving nature of user perceptions in this emerging field. Theoretically, the research enriches the discourse on technology adoption, particularly in virtual environments, by highlighting perceived benefits and enjoyment as significant adoption drivers. These insights are invaluable for stakeholders in the Metaverse Marketplace, guiding the development of more secure, engaging, and user-friendly platforms. While providing a pioneering perspective on Metaverse user perceptions, the study acknowledges its limitation to Twitter data, suggesting the need for broader research methodologies for a more holistic understanding.