Security Breaches Research Articles

Capsule-based and TCN-based Approaches for Spoofing Detection in Voice Biometry

Nowadays, deep neural networks are in a phase of rapid development. Simultaneously, the field of biometric forgery is also advancing. Systems that can successfully pass face verification systems are emerging and continuously improving deepfake videos and voice messages are created. These developments can have a negative impact on a person’s reputation or cause serious security breaches. This paper proposes an approach for spoofing detection in voice biometrics using the ASVspoof2019 LA dataset The model is trained and validated on subsets representing one type of attack, and evaluated on a subset containing more advanced types of spoofing attacks, demonstrating the model’s ability to generalize to more complex attack scenarios. Two models, capsule-based and TCN-based, are proposed, noted as ResCapsGuard and Res2TCNGuard, respectively. ResCapsGuard achieved an Equal Error Rate (EER) value of 2.27, while Res2TCNGuard reached an EER value of 1.49. Notebooks with our models are available in repositories in github. Due to the fact that a random part is cut out of the audio, the results may vary.

AI-driven anomaly detection in cloud computing environments

The rapid adoption of cloud computing has changed the way businesses manage and store data, but it has also introduced new security challenges. One of the most pressing concerns in cloud environments is the detection of anomalies, which can signal potential security breaches, system failures, or performance issues. Traditional anomaly detection methods often fall short due to the complexity, scalability, and dynamic nature of cloud infrastructures. In recent years, Artificial Intelligence (AI)-driven anomaly detection techniques, particularly those leveraging machine learning and deep learning, have shown promise in overcoming these limitations. This paper reviews AI-driven approaches to anomaly detection in cloud computing environments, exploring their applications in enhancing cloud security, optimizing performance, and ensuring efficient resource management. The paper examines the strengths of various AI techniques, including supervised and unsupervised learning, deep learning, and hybrid models, highlighting their capacity to detect complex, previously unknown anomalies. Despite their advantages, implementing AI-based systems in cloud environments presents challenges, including data quality issues, scalability concerns, and computational resource requirements. Solutions such as federated learning and model optimization techniques are explored as methods to address these challenges. Furthermore, the paper discusses future research directions, including the integration of AI-driven anomaly detection with emerging technologies like blockchain and IoT, and the potential for advancements in self-supervised learning and explainable AI (XAI). This review concludes by emphasizing the critical role of AI in securing cloud infrastructures and the promising future of anomaly detection in the cloud computing landscape.

Safety in Connected Health Network: Predicting and Detecting Hidden Information in Data Using Multilayer Perception Deep Learning Model

With the tremendous growth in the use of information technology, the connected health networks are becoming more relevant, greatly improving the traditional standard of healthcare procedures from data acquisition, storage, and sharing among the medics for timely clinical diagnosis processes, therapy, and disease management. However, connected health network comes with network and cyber criminality challenges, and frequent security breach attacks on digital platforms and databases. Unfortunately, Sensitive clinical information is greatly at risk with adversaries, most clinical stakeholders find it difficult to overlook free access to the clinical records. Previously, feature aggregation networks, convolutional neural networks, residual convolutional neural networks, and machine learning models were used in different methodological approaches towards ensuring the detection of hidden information in images, unfortunately, none was able to produce optimal results. This study proposes a three-phased framework to determine the suitability of embedder networks’ feature extraction for image steganalysis, predicting and detecting hidden information in images. A Multilayer Perceptron (MLP) deep learning model was trained for pattern recognition of steganography instances in acquired digital image signals. The digital image signals used for the predictive steganalysis are publicly available images contained in two circumstances highlighted regarding clean image signals (situation of cover images, but without steganography) and the embedded situation of image signals (where images (stego) with hidden data or information). Interestingly, the results of the parameter show that the Max-iter parameter of the MLP classifier hugely determines the performance of the algorithm towards detecting steganography in digital image signals. The parameter stipulates the number of times the training set will pass through the MLP network for the training process. Significantly, in our experiment, Max-iter returned the best result at 1000 netting an accuracy of 93%, precision of 57%, and recall of 100% weighted averages. Our study does not only implement a model that detects hidden information in images, but it also discovered and tuned the multilayer perceptron to determine where it will perform best.

A big data analysis algorithm for massive sensor medical images

Big data analytics for clinical decision-making has been proposed for various clinical sectors because clinical decisions are more evidence-based and promising. Healthcare data is so vast and readily available that big data analytics has completely transformed this sector and opened up many new prospects. The smart sensor-based big data analysis recommendation system has significant privacy and security concerns when using sensor medical images for suggestions and monitoring. The danger of security breaches and unauthorized access, which might lead to identity theft and privacy violations, increases when sending and storing sensitive medical data on the cloud. Our effort will improve patient care and well-being by creating an anomaly detection system based on machine learning specifically for medical images and providing timely treatments and notifications. Current anomaly detection methods in healthcare systems, such as artificial intelligence and big data analytics-intracerebral hemorrhage (AIBDA-ICH) and parallel conformer neural network (PCNN), face several challenges, including high resource consumption, inefficient feature selection, and an inability to handle temporal data effectively for real-time monitoring. Techniques like support vector machines (SVM) and the hidden Markov model (HMM) struggle with computational overhead and scalability in large datasets, limiting their performance in critical healthcare applications. Additionally, existing methods often fail to provide accurate anomaly detection with low latency, making them unsuitable for time-sensitive environments. We infer the extraction, feature selection, attack detection, and data collection and processing procedures to anticipate anomaly inpatient data. We transfer the data, take care of missing values, and sanitize it using the pre-processing mechanism. We employed the recursive feature elimination (RFE) and dynamic principal component analysis (DPCA) algorithms for feature selection and extraction. In addition, we applied the Auto-encoded genetic recurrent neural network (AGRNN) approach to identify abnormalities. Data arrival rate, resource consumption, propagation delay, transaction epoch, true positive rate, false alarm rate, and root mean square error (RMSE) are some metrics used to evaluate the proposed task.

QuIDS: A Quantum Support Vector machine-based Intrusion Detection System for IoT networks

With the increasing popularity of IoT, there has been a noticeable surge in security breaches associated with vulnerable IoT devices. To identify and counter such attacks. Intrusion Detection Systems (IDS) are deployed. However, these IoT devices use device-specific application layer protocols like MQTT and CoAP, which pose an additional burden to the traditional IDS. Several Machine Learning (ML) and Deep Learning (DL) based IDS are developed to detect malicious IoT network traffic. However, in recent times, a variety of IoT devices have been available on the market, resulting in the frequent installation and uninstallation of IoT devices based on users’ needs. Moreover, ML and DL-based IDS must train with sufficient device-specific attack training data for each IoT device, consuming a noticeable amount of training time. To solve these problems, we propose QuIDS, which utilizes a Quantum Support Vector Classifier to classify attacks in an IoT network. QuIDS requires very little training data compared to ML or DL to train and accurately identify attacks in the IoT network. QuIDS extracts eight flow-level features from IoT network traffic and utilizes them over four quantum bits for training. We experimented with QuIDS on two publicly available datasets and found the average recall rate, precision, and f1-score of the QuIDS as 91.1%, 84.3%, and 86.4%, respectively. Moreover, comparing QuIDS with the ML and DL methods, we found that QuIDS outperformed by 37.7%, 24.4.6%, and 36.9% more average recall and precision rates than the ML and DL methods, respectively.

Implementation of Cyber Network’s Attacks Detection System with Deep Learning Designing Algorithms

The internet has become indispensable for modern communication, playing a vital role in the development of smart cities and communities. However, its effectiveness is contingent upon its security and resilience against interruptions. Intrusions, defined as unauthorized activities that compromise system integrity, pose a significant threat. These intrusions can be broadly categorized into host intrusions, which involve unauthorized access and manipulation of data within a system, and network intrusions, which target vulnerabilities within the network infrastructure. To mitigate these threats, system administrators rely on Network Intrusion Detection Systems (NIDS) to identify and respond to security breaches. However, designing an effective and adaptable NIDS capable of handling novel and evolving attack strategies presents a significant challenge. This paper proposes a deep learning-based approach for NIDS development, leveraging Self-Taught Learning (STL) and the NSL-KDD benchmark dataset for network intrusion detection. The proposed approach is evaluated using established metrics, including accuracy, F-measure, recall, and precision. Experimental results demonstrate the effectiveness of STL in the 5-class categorization, achieving an accuracy of 79.10% and an F-measure of 75.76%. This performance surpasses that of Softmax Regression (SMR), which attained 75.23% accuracy and a 72.14% F-measure. The paper concludes by comparing the proposed approach's performance with existing state-of-the-art methods.

An optimized multilayer perceptron-based network intrusion detection using Gray Wolf Optimization

The exponential growth in the use of network services through the design of various network infrastructures, has led to increased complexities and challenges in the network. A major problem in computer networks is privacy and security breach. Cyber attackers exploit loopholes to infiltrate and disrupt the operation of the network through various attacks. Anomaly-based intrusion detection often employs Artificial Neural Network techniques like Multi-layer Perceptron (MLP) to classify malicious and legitimate traffic. Nevertheless, these techniques are vulnerable to overfitting and require extensive labeled data and computational resources. Consequently, this reduces the accuracy of intrusion detection systems and increases the error detection rate. To minimize the error detection rate of the intrusion detection system, it is necessary to optimize the connection parameters of the MLP neural network such as weights and biases. To this end, we proposed an optimized MLP-based Intrusion Detection using Gray Wolf Optimization (GWOMLP-IDS) to optimize the learning process of the MLP neural network by optimizing weights and biases. GWO aims to select an optimal connection parameter during the learning process to minimize the error rate of intrusion detection. Extensive simulations in Python reveal the effectiveness of the proposed approach in terms of designated performance metrics.

SecureCloud Guardian: Advancing Data Security via Advanced Dispersion and Encryption in Cloud Strorage (SCG)

Cloud storage, being one of the foundational pillars of modern computing, has been readily embraced and spurred fast growth in the cloud computing domain. Despite this massive and widespread acceptance, major security issues hang over the cloud storage scene and often result in huge security breaches and leaks of information. SecureCloud Guardian, or SCG, comes into the picture as an important instrument that guarantees privacy in information stored over the cloud. SCG follows a multi-pronged approach, including data dispersal techniques, storage distribution protocols, and encryption formats that guarantee efficient data storage. It also employs a hierarchical management approach that leverages user-level secret code as well as secret division algorithms for maintaining cryptographic material, preventing unauthorized access. The experimental studies carried out on SCG verify the effectiveness of this security-centered cloud storage technology in terms of avoiding data leakage. For instance, SCG uploads and downloads 5G files in a mere 646 seconds and 269 seconds, respectively—an attractive choice for consumers looking for secure cloud information storage.

Predicting Security Breaches in AI-Powered Mobile Cloud Applications Using Deep Random Forest Algorithm

Predicting Security Breaches in AI-Powered Mobile Cloud Applications Using Deep Random Forest Algorithm

A Deep Learning Framework with Learning without Forgetting for Intelligent Surveillance in IoT-enabled Home Environments in Smart Cities

Background: Internet of Things (IoT) technology in smart urban homes has revolutionised sophisticated monitoring. This progress uses interconnected devices and systems to improve security, resource management, and resident safety. Smart cities use technology to improve efficiency, sustainability, and quality. Internet of Things-enabled intelligent monitoring technologies are key to this goal. Objectives: Intelligent monitoring in IoT-enabled homes in smart cities improves security, convenience, and quality of life from advanced technologies. Using live monitoring and risk identification tools to quickly discover and resolve security breaches and suspicious activity to protect citizens. Intelligent devices allow homeowners to remotely control lighting, security locks, and surveillance cameras. Using advanced technologies to regulate heating, cooling, and lighting based on occupancy and usage. Method: This study introduces a deep learning architecture that uses LwF (Learning without Forgetting) to keep patterns while absorbing new data. The authors use IoT devices to collect and analyse data in real-time for monitoring and surveillance. They use sophisticated data preprocessing to handle IoT devices' massive data. The authors train the deep learning model with historical and real-time data and cross-validation to ensure resilience. Result: The proposed model has been validated on two different Robloflow datasets of 7382 images. The proposed model gains an accuracy level of 98.27%. The proposed Yolo-LwF model outperforms both the original Yolo and LwF models in terms of detection speed and adaptive learning. Conclusion: By raising the bar for intelligent monitoring solutions in smart cities, the suggested system is ideal for real-time, adaptive surveillance in IoT-enabled households. By embracing adaptability and knowledge retention, authors envision heightened security and safety levels in urban settings.

HARNESSING PRIVACY-PRESERVING FEDERATED LEARNING WITH BLOCKCHAIN FOR SECURE IoMT APPLICATIONS IN SMART HEALTHCARE SYSTEMS

The Internet of Medical Things (IoMT) refers to interconnected medical systems and devices that gather and transfer healthcare information for several medical applications. Smart healthcare leverages IoMT technology to improve patient diagnosis, monitoring, and treatment, providing efficient and personalized healthcare services. Privacy-preserving Federated Learning (PPFL) is a privacy-enhancing method that allows collaborative method training through distributed data sources while ensuring privacy protection and keeping the data decentralized. In the field of smart healthcare, PPFL enables healthcare professionals to train machine learning algorithms jointly on their corresponding datasets without sharing sensitive data, thereby maintaining confidentiality. Within this framework, anomaly detection includes detecting unusual events or patterns in healthcare data like unexpected changes or irregular vital signs in patient behaviors that can represent security breaches or potential health issues in the IoMT system. Smart healthcare systems could enhance patient care while protecting data confidentiality and individual privacy by incorporating PPFL with anomaly detection techniques. Therefore, this study develops a Privacy-preserving Federated Learning with Blockchain-based Smart Healthcare System (PPFL-BCSHS) technique in the IoMT environment. The purpose of the PPFL-BCSHS technique is to secure the IoMT devices via the detection of abnormal activities and FL concepts. Besides, BC technology can be applied for the secure transmission of medical data among the IoMT devices. The PPFL-BCSHS technique employs the FL for training the model for the identification of abnormal patterns. For anomaly detection, the PPFL-BCSHS technique follows three major processes, namely Mountain Gazelle Optimization (MGO)-based feature selection, Bidirectional Gated Recurrent Unit (BiGRU), and Sandcat Swarm Optimization (SCSO)-based hyperparameter tuning. A series of simulations were implemented to examine the performance of the PPFL-BCSHS method. The empirical analysis highlighted that the PPFL-BCSHS method obtains improved security over other approaches under various measures.

A secure, privacy-preserving, and cost-efficient decentralized cloud storage framework using blockchain

Cloud services benefit countless users worldwide due to notable features, such as on-demand self-service, scalability, easy maintenance, etc. Secure storage and access to data in the cloud is critical. Cloud Identity and Access Management (IAM) service, which acts in a centralized way to provide access requests to the authenticated users. Controlled access sometimes fails to preserve the privacy of the sensitive information stored in the cloud due to several reasons, such as insider attacks, breaches of data security, or any other types of unauthorized access. This paper suggests a blockchain-assisted secure storage and access mechanism to secure sensitive data. Here blockchain is used as a trust management entity that verifies the identity of the user. Along with this it issues the Access Control Lists (ACLs) and identity token, and at the same time, it records all the interactions between the users and service providers. Data transmission is transparent since transactions are recorded. Importance is given to user privacy and decryption keys security. Linear(t,n) secret sharing scheme is used for key share generation and distribution. For experimentation, in MetaMask cryptocurrency wallet Goerli test network is used. Results reveal that our model consumes less cost to execute than other existing works. The total execution cost to upload and download a data file is 0.00281392 and 0.02455307 GoerliETH. Where the all verification operations such as identity token, ACL, access_log, and data integrity are executed in Zero gas value. The proposed model maintains a constant gas cost regardless of transaction volume, with costs of 33.04 ETH and 32.24 ETH for data upload and download. Moreover, we present a comparison of execution time performance in three different system configurations.

Cyberattack event logs classification using deep learning with semantic feature analysis

Event logs play a crucial role in cybersecurity by detecting potentially malicious network activities and preventing data loss or theft. Previous work did not place a high value on log messages and their impact on security breach prediction and intrusion detection. This research paper introduces a novel approach for log message analysis applied to a dataset of event logs collected from various web sources. Event log messages were analyzed and categorized based on event and attack types with an explainable AI emphasizing the value of its key data. The study aims to enhance intrusion detection and minimize performance degradation by identifying suspicious events. In this regard, a new semantic vectorization framework is proposed, leveraging deep learning architectures to develop semantic discriminating log features, offering a cogent explanation and classification of event log messages. The use of BERT deep embeddings as a baseline for the prediction model allows for visualizing and interpreting the formulation of log message semantic features. Several empirical scenarios are set and conducted extensively to evaluate the performance of the event log classifier, considering the attack type, event type, and zero-shot logs. The experimental results demonstrate that the proposed event log classifier outperforms state-of-the-art machine learning models, achieving a recall of 99.27% and a precision of 99.29%. This highlights the model’s ability to accurately identify events of a particular type by detecting as many suspicious events as feasible while minimizing the misclassification rate.

Security compliance and its implication for cybersecurity

Security compliance plays a critical role in shaping and enhancing the cybersecurity posture of organizations. It involves adhering to legal, regulatory, and industry standards that govern data protection, privacy, and security measures. Key regulations, such as GDPR, HIPAA, and PCI DSS, along with international standards like ISO/IEC 27001 and NIST, require organizations to implement security frameworks aimed at managing risks, protecting sensitive data, and ensuring the confidentiality, integrity, and availability of information. The impact of security compliance extends beyond regulatory adherence. By implementing compliance frameworks, organizations enhance their ability to mitigate threats, respond to incidents, and recover from security breaches more effectively. These frameworks help ensure that security measures are consistent, well-documented, and aligned with industry best practices. Additionally, compliance fosters organizational accountability by requiring management oversight and promoting a security-first culture across all levels. However, compliance also presents challenges. Organizations must balance the often resource-intensive process of maintaining compliance with the need for a proactive security strategy that addresses emerging cyber threats. Compliance is sometimes viewed as a "check-the-box" activity, which may lead to a gap between regulatory adherence and actual security needs. Furthermore, the constantly evolving threat landscape requires continuous updates to compliance frameworks, which can be costly and complex, especially for multinational organizations operating under different regulatory regimes. Non-compliance can lead to severe consequences, including legal penalties, financial losses, reputational damage, and operational disruptions. As technology and cyber threats evolve, the relationship between security compliance and cybersecurity will continue to grow in importance, with a greater focus on integrating risk-based approaches and automation into compliance management.

The Role of Technology in The Transformation of Accounting Financial Audit Case Study of Blockchain Implementation in The Audit Process

With the increasing number of cyber attacks and data security breaches, the protection of financial information is becoming increasingly important. Conventional financial audit systems may not have adequate mechanisms to protect sensitive data from the rapidly evolving digital security threats. The aim of this research is to determine the role of technology, particularly blockchain technology, in transforming the financial audit process in accounting practice, and to understand the impact of implementing blockchain technology in the financial audit process on the efficiency, transparency, and accuracy of financial information. The research method used in this study is a Systematic Literature Review (SLR). The results of this study indicate that the use of blockchain technology in financial audit practice provides an effective solution for improving the integrity, reliability, and quality of financial reports presented by companies. Keywords: technology, financial audit, blockchain

Fortifying cloud environments against data breaches: A novel AI-driven security framework

As cloud computing continues to dominate the modern technological landscape, organizations face growing challenges in preventing data breaches and sophisticated cyber threats. The increasing complexity and scale of cloud environments require advanced security mechanisms to address evolving threats. This paper introduces "SecureCloudAI," a cutting-edge AI-driven security framework designed to fortify sensitive data within cloud infrastructures. SecureCloudAI leverages a hybrid approach that combines machine learning models like Random Forest and deep learning techniques, including Long Short-Term Memory (LSTM) networks, to detect, classify, and respond to potential security breaches in real-time. The system offers robust malware detection, network traffic analysis, and web intrusion detection while maintaining scalability and efficiency across large cloud environments. Experimental results demonstrate SecureCloudAI's high accuracy, with malware detection at 94.78% and network traffic classification at 90.92%, ensuring the system can handle both complex and emerging threats. This AI-driven solution marks a significant advancement in cloud security, providing organizations with an adaptive and scalable tool to safeguard their data against the ever-growing cyber threat landscape.

Physical Patrol and Border Security Management at the Namanga One Stop Boarder Post, Kenya

Border administration encompasses all legal and administrative measures a sovereign state implements to secure, protect, and strengthen the sovereignty, security, safety, and integrity of its territory. Consequently, it is essential to any national migration strategy. The border administration policies emphasize the necessity for all participants in border management to be engaged to enhance the effectiveness of border operations. Security measures are normally enhanced along the border posts to improve on the national security of a given country by preventing entrance of terrorists, illegal goods and human trafficking. These security measures ensure safety at the border. The primary objective was to evaluate the effects of physical patrol on border security management at the Namanga one stop boarder post, Kenya. A descriptive survey research design was utilized. The population comprised of the employees at the Namanga OSBP, including police officers, customs officials, immigration officers, and National Intelligence Service employees, among others. The research used stratified random sampling. The data was collected via the use of questionnaires. Data analysis was done with the help of SPSS and then presented using tables, percentages, graphs and pie charts. This study found out that border security is complex and it requires various interventions, which entails a combination of technology and human security personnel for effective operations. In regard to objective one, the researcher finds that physical patrol emerges as a fundamental border security management measure, and is considered essential in enhancing security at the border point by minimizing insecurity incidents and eliminating potential of border security breaches. Based on the findings, this study concluded that various security measures, including physical patrols, integrated scanning, and electronic monitoring, are essential in the management of border security and in enhancing services. A major recommendation of the study is to promote greater multi-agency and multi-stakeholder collaboration as a means of detaining individuals attempting to enter a country illegally and effectively seizing any contraband goods. This necessitates more efficient border patrols and surveillance strategies.

A Welfare Test for Sharing Health Data

AbstractMachine learning and artificial intelligence methods are being increasingly used in the analysis of patient level and other health data, resulting in new insights and significant societal benefits. However, researchers are often denied access to sensitive health data due to concerns on privacy and security breaches. A key reason for reluctance in data sharing by data custodians is the lack of a specific legislative or other methodological framework in balancing societal benefits against costs from potential privacy breaches. Consistent with traditional cost–benefit analysis and the use of Quality Adjusted Life Years (QALYs), this study proposes an economic test that would enable Research Ethics Boards (REBs) to assess the benefits versus risks of data sharing. Our test can also be used to assess whether REBs employ reasonable effort to protect individual privacy and avoid tort liability. Constructing an acceptable welfare test for health data sharing will also lead to a better understanding of the value of data.

Creation of an Anomaly Detection System for Blockchain-based Cloud Deployments that Consider Privacy and Delay.

Cloud-based deployments are increasingly vulnerable to many kinds of assaults; hence powerful anomaly detection systems are needed to prevent security breaches. While effective to some degree, current methods like RSSI, GTM, and APG have drawbacks in terms of scalability, precision, and accuracy. In order to improve the forecast accuracy of cloud assaults and optimize blockchain-based cloud installations, this study suggests a novel anomaly detection system that combines multimodal feature analysis, deep learning models, and QoS-aware sidechains. The suggested strategy considerably surpasses traditional approaches in terms of precision, accuracy, recall, and AUC performance by optimizing feature variance across various sample types and utilizing cutting-edge deep learning algorithms. Additionally, the framework shows increased efficiency in terms of throughput, energy usage, and block mining latency, makingcombining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), the system analyses system logs to find anomalous patterns of activity linked to various threats. The architecture guarantees the transparency and integrity of system records through the use of Blockchain technology, while Deep Learning models offer accurate and quick anomaly detection. The advantages of both Deep Learning and Blockchain technology support the choice to merge them. Blockchain technology ensures the accuracy of anomaly detection and prevents tampering with system records by providing a distributed, immutable ledger. On the other hand, deep learning models produce high precision, accuracy, recall, and AUC measures because of their remarkable pattern recognition skills and ability to adjust to shifting attack strategies. Experimental data analyses show the effectiveness of the suggested framework.

Blockchain and Digital Twins in Smart Industry 4.0: The Use Case of Supply Chain-A Review of Integration Techniques and Applications

The Fourth Industrial Revolution has transformed industries and supply chains by integrating advanced operations, tools, and logistics services. Despite these advancements, challenges persist, particularly in ensuring data dependability, security, and operational efficiency. Digital twins (DTs), which replicate real-world components and processes, have emerged as essential tools for enhancing predictive analytics, simulation, and product lifecycle management in Industry 4.0. However, traditional DT development relies on centralized systems, which are vulnerable to data tampering and security breaches, especially in the management of transaction logs and historical data. To address these challenges, this review provides a comprehensive analysis of the current state of integrating blockchain with DTs. Using a qualitative research methodology, including desk research, case studies, and interviews with industry experts, we analyze various blockchain-based DT applications across industries and specifically in supply chain management. The findings reveal that blockchain-enhanced DTs can significantly improve data integrity, traceability, and security, thus boosting operational efficiency and quality control in supply chains. Additionally, this study identifies key integration techniques and the role of blockchain in automating processes through smart contracts. This review provides insights into the practical implications of blockchain-based DTs, highlighting their potential to enhance the reliability and scalability of Industry 4.0 operations.