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Smart contracts are changing many business areas with blockchain technology, but they still have vulnerabilities that can cause major financial losses. Because deployed smart contracts (SCs) are irreversible once deployed, fixing these vulnerabilities before deployment is critical. This research introduces a new method that combines code embedding with Generative Adversarial Networks (GANs) to find integer overflow vulnerabilities in smart contracts. Using Abstract Syntax Trees, we can vectorize the source code of smart contracts while keeping all of the important contract characteristics and going beyond what can be achieved with conventional textual or structural analysis. Synthesizing contract vector data using GANs alleviates data scarcity and facilitates source code acquisition for training our detection system. The proposed method is very good at finding vulnerabilities because it uses both GAN discriminator feedback and vector similarity measures based on cosine and correlation coefficients. Experimental results show that our GAN-based proactive analysis method achieves up to 18.1% improvement in accuracy over baseline tools such as Oyente and sFuzz.

The rapid advancement of key technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and edge-cloud computing has significantly accelerated the transformation toward smart industries across various domains, including finance, manufacturing, and healthcare. Edge and cloud computing offer low-cost, scalable, and on-demand computational resources, enabling service providers to deliver intelligent data analytics and real-time insights to end-users. However, despite their potential, the practical adoption of these technologies faces critical challenges, particularly concerning data privacy and security. AI models, especially in distributed environments, may inadvertently retain and leak sensitive training data, exposing users to privacy risks in the event of malicious attacks. To address these challenges, this study proposes a privacy-preserving, service-oriented microservice architecture tailored for intelligent Industrial IoT (IIoT) applications. The architecture integrates Differential Privacy (DP) mechanisms into the machine learning pipeline to safeguard sensitive information. It supports both centralised and distributed deployments, promoting flexible, scalable, and secure analytics. We developed and evaluated differentially private models, including Radial Basis Function Networks (RBFNs), across a range of privacy budgets ( ), using both real-world and synthetic IoT datasets. Experimental evaluations using RBFNs demonstrate that the framework maintains high predictive accuracy (up to 96.72%) with acceptable privacy guarantees for budgets . Furthermore, the microservice-based deployment achieves an average latency reduction of 28.4% compared to monolithic baselines. These results confirm the effectiveness and practicality of the proposed architecture in delivering privacy-preserving, efficient, and scalable intelligence for IIoT environments. Additionally, the microservice-based design enhanced computational efficiency and reduced latency through dynamic service orchestration. This research demonstrates the feasibility of deploying robust, privacy-conscious AI services in IIoT environments, paving the way for secure, intelligent, and scalable industrial systems.

Recent advances in artificial intelligence have greatly increased the accuracy of computer-assisted diagnosis for serious conditions including brain tumours. However, concerns about data privacy, class imbalance, and the diversity of medical datasets limit the application of centralised deep learning models in healthcare. This article introduces MedShieldFL, a hybrid privacy-preserving federated learning architecture that enables secure and decentralised brain tumour classification across many medical institutions. The approach uses data augmentation techniques to reduce class imbalance and homomorphic encryption to safely aggregate model changes while safeguarding sensitive patient data. The basic model is a ResNet-18-based classifier that strikes the ideal balance between accuracy and speed. The test results for MedShieldFL show that it can accurately group data into 93% to 96% of the time. This approach improves performance by about 2% compared to traditional federated learning models and keeps data privacy safe enough. The framework makes sure that the extra work that encryption adds to real-world programs stays within acceptable limits. This keeps execution times fair. Medical picture evaluation with MedShieldFL is a useful and flexible technology that protects privacy. This makes it easier for current healthcare systems to use AI that is safe and works with other AI.

Wireless sensor networks (WSNs) will be necessary for the next generation of Internet of Things (IoT) apps. They make it possible to use smart and long-lasting sensors and smart automation in healthcare, Industry 4.0, and critical infrastructure. But security is particularly hard since they have built-in flaws, not enough computer power, not enough energy, and a significant danger of insider threats. Standard encryption methods aren’t enough, and in situations where resources are restricted, heavier blockchain or machine learning solutions aren’t always possible. This study presents ChainShieldML, a lightweight hybrid security architecture that combines Blockchain (BC) and machine learning (ML) to provide decentralised, adaptive, and resource-efficient protection for wireless sensor networks (WSNs). The idea is based on a two-pronged defence strategy. The Blockchain Prevention Module’s permissionless blockchain architecture for base stations and cluster heads makes it possible to verify identities, maintain trust in a decentralised way, and keep node interactions unchangeable. Smart contracts made in solidity and connected to the Ethereum ecosystem make it possible to safely register nodes and keep an eye on what they do. The VBFT consensus algorithm makes it possible to quickly validate without using as much computing power as most proof of work methods. The machine learning detection module uses the lightweight gradient boosting method (LightGBM) to find and rank dangerous nodes in real time. LightGBM is the best machine learning classifier when looking at things like recall, F1-score, Matthews correlation coefficient, training cost, and inference latency. ChainShieldML dramatically improves the detection of insider attacks, builds trust, and protects data while using very little energy and having very little communication delay, as shown in tests. For Wireless sensor networks (WSNs) to keep working, all of these things are very important. ChainShieldML is a novel solution to keep IoT devices safe. It uses blockchain’s decentralised trust and ML’s adaptive intelligence to make a defence system for next-generation wireless sensor networks that can grow, is strong, and is ready for the future.

The paper examines three renowned blockchain systems, Bitcoin, Ethereum and Hyperledger Fabric, with the aim of offering a comprehensive understanding of how consensus and fault tolerance synergistically contribute to the reliability and resilience of networks. Additionally, case studies of historical incidents and system upgrades within each blockchain system offer a qualitative lens to view the evolution and adaptation of consensus and fault tolerance. The results illuminate the intricate interaction between decentralized agreement algorithms and resilient strategies. The study adds a deeper comprehension of how these aspects collectively impact the dependability, safety and stability of blockchain networks. This investigation contributes to the broader discussion surrounding decentralized systems, providing valuable perspectives for researchers, practitioners, and policymakers as they navigate the constantly evolving realm of blockchain technology.

Global economies are continually threatened by financial frauds and crimes, resulting in large financial losses and ersosion of public confidence in banking institutions. Conventional methods of identifying and preventing fraud often prove ineffective due to their reactive nature and incapacity to handle large-scale transactions. This paper investigates the potential ofblockchain technology as a prompt resolution for detecting and preventing financial fraud. Blockchain's inherent features of decentralization, transparency, and immutability can establish a strong framework for secure and transparent financial transactions. By utilizing the distributed ledger and smart contract features of blockchain, it is feasible to create a system that can identify fraudulent activities in real-time and prevent their occurrence. The paper explores the workings of such a system, discusses its potential advantages and challenges, and provides insights into its practical implementation. The results could herald a new age in financial security, with blockchain technology playing a crucial role in combating financial fraud and crime. In addition, the paper also delves into the integration of Hyperledger, a permissioned blockchain framework, as a strategic component in the development of the proposed system, enhancing the security and efficiency of global financial transactions.