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The gaming industry, which predominantly depends on centralized platforms, faces growing challenges in protecting digital assets, ensuring gameplay fairness, and addressing online fraud. Conventional gaming systems store player data on centralised servers, including sensitive financial and personal information, making them particularly vulnerable to cyberattacks and security breaches. With gaming transactions frequently occurring on unsecured mobile and desktop platforms, player accounts are often at risk of theft, hacking, and fraud. These security vulnerabilities result in significant financial losses for players and developers, underscoring the urgent need for a secure, transparent, decentralized solution. In light of these challenges, adopting blockchain technology in gaming platforms has emerged as a promising and transformative solution. Blockchain’s decentralized and immutable ledger offers enhanced security, making it nearly impossible for malicious actors to manipulate transaction records or steal digital assets. Research and practical implementations have shown that blockchain can effectively reduce online fraud by securely storing in-game assets and transactions on decentralized networks, ensuring their integrity and resistance to tampering. This paper makes several significant contributions to understanding how blockchain technology can revolutionize the gaming industry. First, it provides a comprehensive analysis of blockchain’s security advantages, emphasizing its capacity to reduce online fraud and enhance the protection of digital assets. Second, the paper explores how blockchain enhances player autonomy by establishing secure digital identities and tokenising in-game assets. By enabling players to own, trade, and exchange their assets directly, blockchain fosters a decentralized gaming economy that grants players greater control over their virtual possessions. Finally, the paper identifies emerging trends and potential research avenues in blockchain-enabled gaming, offering valuable insights into the challenges and opportunities in this rapidly evolving field.

The Industrial Internet of Things Networks (IIoT-N) have revolutionized industrial systems by connecting sensors, devices, and data analytics, creating complex, data-driven environments. However, key challenges persist, such as data diversity, scalability issues, sparse data, anomaly detection, and adapting to changing conditions while managing limited resources. To address these, Transfer Learning, a machine learning technique, has become a practical solution, enhancing IIoT-N by enabling better data integration, real-time analytics, improved fault detection, and adaptable models with minimal retraining, all while optimizing resource usage. This paper examines the role of Transfer Learning in IIoT-N, identifying research gaps and challenges, and highlights its potential across various industrial applications, including predictive maintenance, anomaly detection, edge computing, process optimization, and cross-domain knowledge transfer. Special attention is given to manufacturing, where Transfer Learning shows significant promise. The study also proposes a taxonomy of approaches for integrating Transfer Learning into IIoT-N. These include Domain Adaptation Techniques, Data Augmentation and Synthesis, Hybrid Models using Ensemble Learning, and practical strategies for successful implementation. Additionally, a detailed case study demonstrates the real-world application of Transfer Learning in industrial networks, showcasing its practical benefits. Thus, this paper contributes to the advancement of Transfer Learning in IIoT-N by providing insights into its potential to enhance industrial processes and address existing challenges. It offers theoretical and practical perspectives on how Transfer Learning can be effectively applied in industrial environments, driving efficiency and adaptability.

The growing complexities in supply chain management, driven by internationalization, decentralization, and modernization, are increasingly threatened by data breaches, network intrusions, and software vulnerabilities. Business stakeholders are turning to blockchain technology as a possible solution to improve modern supply chains’ reliability, integration, and visibility. Blockchain provides a robust framework that supports product traceability, seamless system integration, and enhanced transparency across operations. However, despite its transformative potential, integrating blockchain into supply chain management (SCM) introduces new security risks. These include threats to the confidentiality of blockchain transactions, as attackers may employ techniques like transaction linkage and deanonymization. This paper explores the specific security challenges related to blockchain-based SCM systems, including risks related to SCM, Internet of Things (IoT) system attacks, and blockchain system vulnerabilities. Additionally, the paper presents a case study on securing the drug supply chain through blockchain technology, focusing on attribute-based identity management and zero-knowledge proofs (ZKP) to bolster security and transparency. This approach highlights how blockchain can enhance data privacy, improve access control and identity management, protect information integrity and traceability, and reduce the risk of counterfeit products throughout the supply chain. The paper provides valuable guidance and outlines potential directions for future research in blockchain-driven security and associated issues for supply chain networks, offering a clear roadmap for researchers.

The Internet of Things (IoT) and advanced human-centred technologies have seen rapid expansion in smart city applications, including smart enterprises, homes, and vehicular networks. The IoT architecture underpins technological support for both human- and device-centric secure solutions, which are essential to the functioning of smart cities. Within this evolving landscape, Vehicle-to-Everything (V2X) technology has emerged as a key innovation, integrating vehicular communication with internet connectivity. This integration facilitates seamless data exchange between vehicles, infrastructure, and other entities, contributing to enhanced road safety, optimized traffic flow, reduced emissions, and the development of more intelligent transportation systems. As V2X technology becomes increasingly widespread, ensuring the security and privacy of the information exchanged within these networks is critical. While much of the existing research has concentrated on general IoT security frameworks and standalone vehicular communication protocols, our study addresses the unique security challenges inherent in V2X environments. This paper identifies and examines various security threats, including data privacy breaches, malicious attacks, and traffic manipulation, and explores strategies to mitigate these risks within V2X communications. In addition, this paper investigates the potential of blockchain technology as an innovative solution for enhancing the security and trustworthiness of information exchanges in V2X networks. The paper also includes a case study that presents a proposed solution demonstrating how blockchain technology could strengthen the security and integrity of data transfers within V2X systems. Through comprehensive analysis, evaluation, and recommendations for future research, this work contributes to the current understanding of secure data exchange in V2X networks.

With the growth of the internet, the landscape of commercial transactions has undergone significant changes. The digital revolution has permeated various aspects of our daily lives, notably transforming the financial system through digital money transfers across global distances. Digital transactions, including online purchases, money transfers, banking, and investing, have become routine. This shift has provided many advantages and opened the door for illegal activities such as money laundering. This research aims to identify transaction patterns to help algorithms recognize and flag scammers. We propose a deep learning-based sequential model framework to optimize fraud and non-fraud transaction classification. Proposed approach incorporates methods of ensemble learning including Gradient Boost, Random Forest, Logistic Regression, and Voting Classifiers, along with hyperparameter tuning to prevent overfitting. To solve the dataset inconsistency common in credit card dataset, we used techniques like under-sampling and the Synthetic Minority Over-sampling Technique (SMOTE) with specific machine learning (ML) algorithms. Our methodology involves data pre-processing, feature engineering, model selection, and evaluation. We leveraged the computational power of Google Colab for efficient model training and testing. Temporal features were extracted to capture transaction patterns over time, such as frequency and timing, along with features based on transaction amounts, merchant types, and geographic locations to detect subtle anomalies indicative of fraud. These features were input into a supervised learning algorithm, resulting in a model significantly outperforming baseline methods. The model was tested using a sizable dataset of anonymized transactions made with credit cards, and its effectiveness was measured using metrics such as accuracy, precision, recall, and F1-score. While it cannot entirely prevent fraudulent transactions, it effectively manages false positives through careful review, achieving an accuracy of 99.59%, surpassing other innovative models.

Automating industrial tasks have become critical for organizations due to the inefficiencies and risks associated with conventional procedures. The proliferation of connected devices and machines brings forth a range of security concerns, including data tampering and unauthorized access. Establishing confidential and trustworthy communication between these devices becomes particularly difficult as they rely on the open channel of the Internet. Numerous established security measures, such as antivirus software, access control systems, intrusion detection systems (IDS), and intrusion prevention systems (IPS), have been identified in the literature as facing issues like centralized vulnerabilities, latency challenges, reliability issues, and single points of failure. As technology advances rapidly, the convergence of emerging technologies holds immense potential to revolutionize industrial automation. Among these promising technologies, blockchain and 6G stand out for their immense potential. With its decentralized and tamper-proof nature, blockchain has disrupted various sectors, while 6G offers unprecedented connectivity and lightning-fast data transfer speeds. Motivated by these developments, this paper explores the potential impact of integrating blockchain technology with 6G in industrial automation, paving the way for the future of smart factories and intelligent supply chains. Our proposed work aims to provide a holistic understanding of this emerging amalgamation’s key benefits, challenges, offered solutions, and prospects.

Development of the efficient power aware protocol is the need of today s adhoc networks. Although developing battery efficient systems that have low cost and complexity, remains a crucial issue. In order to facilitate communication within a mobile adhoc network, an efficient routing protocol is required to discover routes between mobile nodes. Power is one of the most important design criteria for adhoc networks as batteries provide limited working capacity to the mobile nodes. Power failure of a mobile node not only affects the node itself but also its ability to forward packets on behalf of others and hence affects the overall network lifetime. Much research efforts have been devoted to develop energy aware routing protocols. In this paper we propose an efficient algorithm, which maximizes the network lifetime by minimizing the power consumption during the source to destination route establishment. As a case study proposed algorithm has been incorporated along with the route discovery procedure of AODV and by simulation it is observed that proposed algorithm s perperformance is better as compare to AODV and DSR in terms of various energy related parameters like Total Energy Consumption, Average Energy Left Per Alive Node, Node Termination Rate, and Network Lifetime for different network scenarios.