The Internet of Things (IoT) is a constantly expanding system connecting countless devices for seamless data collection and exchange. This has transformed decision-making with data-driven insights across different domains. However, challenges arise concerning security and computational limitations. To strengthen IoT against cyber threats and optimize resource usage, combining Quantum Computing with Machine Learning (ML) is a promising approach. ML enables computers to learn from data and detect patterns without explicit programming. By leveraging ML algorithms, vast datasets from IoT devices can be analyzed, identifying anomalies and forecasting potential security breaches. Yet, conventional ML algorithms may need help with the complexity and scale of IoT data. Quantum Computing, based on quantum mechanics, offers unparalleled computational speed and scale. Quantum ML algorithms can quickly analyze IoT datasets, identifying patterns and potential threats. This study examines the ideas behind ML, quantum computing, and their potential collaboration within IoT networks. The research focuses on the possibility of improving the security of IoT networks by integrating quantum computing approaches with ML. It also addresses the challenges and limitations of integrating ML and quantum computing in the context of IoT networks. These obstacles include hardware constraints, algorithm complexities, and the need for specialized knowledge.

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.

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.

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.