Data Security Challenges Research Articles

OPTIMIZATION OF CYBER-PHYSICAL SYSTEM PARAMETERS BASED ON INTELLIGENT IOT SENSORS DATA

The optimization of cyber-physical systems (CPS) parameters is researched with focusing on the integration of computation, networking, and physical processes with the Internet of Things (IoT). The use of intelligent IoT sensors is crucial for collecting real-time data, which is necessary for enhancing the efficiency, reliability, and performance of CPS. Various methods of CPS parameters optimization are analyzed and categorized into model-based approaches, data-driven approaches, and hybrid approaches. The model-based approaches rely on mathematical models to describe CPS behavior and use optimization algorithms like linear programming and evolutionary algorithms to predict system responses and optimize parameters. But, the limitations of model-based approaches are related to complex systems with uncertain or dynamic behavior. The data-driven approaches are more suitable for complex cyber-physical systems. These approaches utilize machine learning and data analytics techniques to extract patterns from sensor data, which are then used to adjust system parameters. The hybrid approaches combine elements of both model-based and data-driven methods. The method of cyber-physical system parameters optimization based on intelligent IoT sensors data processing is developed with using of distributed neural network. The optimization problem is formulated with constraints for the system parameters. The neural network mathematical model and learning algorithm are proposed. The performed research shows the importance of developing optimization methods for CPS parameters based on intelligent IoT sensor data, considering the evolving nature of IoT technology. The integrating intelligent sensors into CPS offers new opportunities for optimizing system performance but also presents challenges in data management and security that should be addressed in future.

Unveiling the core of IoT: comprehensive review on data security challenges and mitigation strategies

The Internet of Things (IoT) is a collection of devices such as sensors for collecting data, actuators that perform mechanical actions on the sensor's collected data, and gateways used as an interface for effective communication with the external world. The IoT has been successfully applied to various fields, from small households to large industries. The IoT environment consists of heterogeneous networks and billions of devices increasing daily, making the system more complex and this need for privacy and security of IoT devices become a major concern. The critical components of IoT are device identification, a large number of sensors, hardware operating systems, and IoT semantics and services. The layers of a core IoT application are presented in this paper with the protocols used in each layer. The security challenges at various IoT layers are unveiled in this review paper along with the existing mitigation strategies such as machine learning, deep learning, lightweight encryption techniques, and Intrusion Detection Systems (IDS) to overcome these security challenges and future scope. It has been concluded after doing an intensive review that Spoofing and Distributed Denial of Service (DDoS) attacks are two of the most common attacks in IoT applications. While spoofing tricks systems by impersonating devices, DDoS attacks flood IoT systems with traffic. IoT security is also compromised by other attacks, such as botnet attacks, man-in-middle attacks etc. which call for strong defenses including IDS framework, deep neural networks, and multifactor authentication system.

A Comparison of Enhanced Ensemble Learning Techniques for Internet of Things Network Attack Detection

Over the past few decades, the Internet of Things (IoT) has become increasingly significant due to its capacity to enable low-cost device and sensor communication. Implementation has opened up many new opportunities in terms of efficiency, productivity, convenience, and security. However, it has also brought about new privacy and data security challenges, interoperability, and network reliability. The research issue is that IoT devices are frequently open to attacks. Certain machine learning (ML) algorithms still struggle to handle imbalanced data and have weak generalization skills when compared to ensemble learning. The research aims to develop security for IoT networks based on enhanced ensemble learning by using Grid Search and Random Search techniques. The method used is the ensemble learning approach, which consists of Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost). This study uses the UNSW-NB15 IoT dataset. The study's findings demonstrate that XGBoost performs better than other methods at identifying IoT network attacks. By employing Grid Search and Random Search optimization, XGBoost achieves an accuracy rate of 98.56% in binary model measurements and 97.47% on multi-class data. The findings underscore the efficacy of XGBoost in bolstering security within IoT networks.

Adoption and Impact of Cloud Computing in Small and Medium Enterprises A Systematic Review

This research aims to investigate the adoption and impact of cloud computing on Small and Medium Enterprises (SMEs) through a systematic approach. The research method involves analyzing a number of relevant and verified scientific articles, focusing on technology adoption, benefits gained, and challenges faced by SMEs in adopting cloud computing technology. The analysis results show that cloud computing adoption among SMEs has significantly increased in recent years, with benefits including operational efficiency, scalability, and data accessibility. However, there are some obstacles faced, including data security issues, resource limitations, and challenges in integrating with existing systems. In conclusion, although cloud computing adoption holds great potential benefits for SMEs, associated challenges need to be addressed with careful strategies.

Large file encryption in a reduced-round permutation-based AES file management system

In the rapid evolving digital landscape, the imperative to ensure data security has never been more crucial. This paper addresses the pressing challenges in data security by introducing a file encryption management system, leveraging a modified advanced encryption standard (AES) algorithm with reduced round iterations and bit permutation. This system aims to comprehensively secure various file types, providing a dependable solution for file exchange. Our findings reveal substantial improvements in both encryption and decryption processes using the reduced-round permutation-based AES (RRPBA). The adapted algorithm demonstrates a significant 38.8% acceleration in encryption time and a remarkable 44.86% improvement in decryption time, positioning it as a pivotal component for efficient file operations within the management system. Moreover, the throughput assessments showcase a remarkable 33.73% improvement in encryption and 23.72% in decryption, outperforming the original AES, emphasizing the algorithm's superior computational effectiveness, signaling positive implications for future high-performance applications. In conclusion, the study not only addresses critical security challenges but also presents a viable solution with tangible speed advantages for file encryption and decryption processes within digital file management systems.

Voice assistants and U.S. consumer behavior: A comprehensive review: investigating the role and influence of voice-activated technologies on shopping habits and brand loyalty

This study provides a comprehensive review of the influence of voice-activated technologies, particularly voice assistants, on consumer behavior and brand loyalty in the United States. The primary objective is to understand how these emerging technologies are reshaping shopping habits and the dynamics of brand-consumer interactions. The methodology encompasses a thorough analysis of peer-reviewed academic journals, industry reports, and case studies, supplemented by secondary sources such as market analyses and news articles. Key findings reveal that voice assistants have induced a significant paradigm shift in consumer behavior, characterized by changes in purchasing patterns, decision-making processes, and brand interactions. The convenience and personalized experience offered by these technologies have led to increased impulse buying and brand experimentation. However, this shift is accompanied by challenges in data privacy and security. The study also highlights the rapid evolution of voice-activated consumer engagement, with advancements in AI and machine learning enhancing personalization and efficiency. Strategically, the study recommends that brands and retailers invest in voice search optimization, develop secure data practices, and collaborate with technology developers to create engaging consumer experiences. The study concludes that while voice assistants present substantial opportunities for enhanced consumer engagement, they also pose challenges that require careful navigation, particularly in maintaining consumer trust and ethical standards. Future research directions include exploring the long-term effects of voice-activated shopping on consumer loyalty and the impact of demographic factors on the adoption of voice assistants. Keywords: Voice Assistants, Consumer Behavior, Brand Loyalty, Voice-Activated Technologies.

ADVANCING DATA SECURITY IN GLOBAL BANKING: INNOVATIVE BIG DATA MANAGEMENT TECHNIQUES

In the context of the rapidly evolving digital landscape, the banking sector faces unprecedented challenges in data security due to the massive volumes of sensitive information they manage. This paper examines the implementation and efficacy of innovative big data management techniques within global banking institutions to enhance data security. It discusses the integration of predictive analytics, the impact of regulatory changes, and the adoption of emerging technologies like blockchain and advanced encryption, which collectively redefine data security strategies. The study utilises a qualitative case study approach focusing on three central banks—JPMorgan Chase & Co., HSBC Holdings plc, and the Industrial and Commercial Bank of China—highlighting how each bank utilises big data techniques to address specific security challenges, comply with regulations, and enhance customer trust. The findings underscore the crucial role of innovative data management strategies in mitigating risks and safeguarding data against cyber threats, suggesting that these technologies fulfil security needs and offer competitive advantages in customer trust and regulatory compliance. The paper concludes with strategic recommendations for banks to enhance their data security measures and suggests directions for future research in data security within the banking industry.

Data sovereignty and security in network engineering: A conceptual framework for compliance

Data sovereignty and security are critical issues in network engineering, particularly in an era of increasing data breaches and regulatory scrutiny. This concept paper presents a conceptual framework for understanding and ensuring compliance with data sovereignty and security requirements in network engineering. The framework begins by defining data sovereignty and security in the context of network engineering, highlighting the importance of protecting data from unauthorized access and ensuring that it is stored and processed in compliance with applicable laws and regulations. Next, the framework explores key concepts and principles related to data sovereignty and security, such as data localization, encryption, and access control. It also discusses the role of international agreements and standards in shaping data sovereignty and security requirements. The framework then guides how organizations can implement measures to protect data sovereignty and security in their network engineering practices. This includes conducting risk assessments, implementing appropriate security controls, and ensuring compliance with relevant laws and regulations. Finally, the framework outlines future trends and challenges in data sovereignty and security, such as the impact of emerging technologies like 5G and the Internet of Things (IoT) on data protection requirements. Overall, this concept paper provides a comprehensive overview of data sovereignty and security in network engineering, offering practical guidance for organizations seeking to protect their data and comply with relevant laws and regulations.

Developing a Novel Ontology for Cybersecurity in Internet of Medical Things-Enabled Remote Patient Monitoring.

IoT has seen remarkable growth, particularly in healthcare, leading to the rise of IoMT. IoMT integrates medical devices for real-time data analysis and transmission but faces challenges in data security and interoperability. This research identifies a significant gap in the existing literature regarding a comprehensive ontology for vulnerabilities in medical IoT devices. This paper proposes a fundamental domain ontology named MIoT (Medical Internet of Things) ontology, focusing on cybersecurity in IoMT (Internet of Medical Things), particularly in remote patient monitoring settings. This research will refer to similar-looking acronyms, IoMT and MIoT ontology. It is important to distinguish between the two. IoMT is a collection of various medical devices and their applications within the research domain. On the other hand, MIoT ontology refers to the proposed ontology that defines various concepts, roles, and individuals. MIoT ontology utilizes the knowledge engineering methodology outlined in Ontology Development 101, along with the structured life cycle, and establishes semantic interoperability among medical devices to secure IoMT assets from vulnerabilities and cyberattacks. By defining key concepts and relationships, it becomes easier to understand and analyze the complex network of information within the IoMT. The MIoT ontology captures essential key terms and security-related entities for future extensions. A conceptual model is derived from the MIoT ontology and validated through a case study. Furthermore, this paper outlines a roadmap for future research, highlighting potential impacts on security automation in healthcare applications.

Designing Integrated IT Architecture for Health Monitoring Internet of Things: Findings Exploratory Study

IT integration with healthcare, mainly through Internet of Things-based health monitoring systems, is crucial to improving healthcare management in the digital age. However, challenges remain in the design of an integrated IT architecture that can support the sustainability and effectiveness of IoT health monitoring systems, which still need to be addressed. The shortcomings in the literature related to the application of a holistic IT architecture framework to address these challenges indicate a knowledge gap that needs to be filled. Through the application of the TOGAF methodology, this research seeks to design and analyze an integrated IT architecture for IoT-based health monitoring systems in Indonesia, taking a qualitative approach through case studies, in-depth interviews, and document analysis. The main findings show that the application of the TOGAF framework successfully addresses the challenges of interoperability, data security, and system scalability by effectively integrating IoT technologies in the healthcare environment and considering the local social and infrastructural context. The implementation of the IT architecture developed based on the TOGAF methodology demonstrated improved coordination between IoT devices and backend systems, facilitated secure and real-time data flow, and accommodated the scalability and sustainability needs of the system. The findings have significant implications in supporting the development of more efficient and effective health monitoring systems, offering strategic guidance for system developers, policymakers, and IT practitioners within the healthcare sector.

Review on Data Privacy, Protection, and Security Challenges in Blockchain Adoption Across Diverse Domains

The paper explores how blockchain technology can transform data management, especially when it comes to handling privacy and security issues. Blockchain's immutability, transparency, and decentralization make it a potentially useful tool for addressing the expanding problems associated with data security and integrity. However, the slow adoption of blockchain technology is influenced by a complex interplay of developing privacy concerns, legislative uncertainty, and technological subtleties. The current study aims to identify key data privacy, protection, and security challenges associated with blockchain adoption in various domains, assess the adequacy of the current data privacy, protection, and security laws to address these challenges, and propose potential solutions and areas for further research to mitigate data privacy, protection, and security risks in blockchain applications. Additionally, the research has examined the adaptability and dependability of the two main kinds of blockchain—public and private—as well as the variations in their reach and visibility.

Data Transmission Security on Cloud Computing using Algorithm

Cloud computing enables businesses to efficiently store vast amounts of data in the cloud, which can be accessed seamlessly over the Internet without hardware compatibility constraints. However, the transmission of data over the Internet is susceptible to various security threats such as man-in-the-middle attacks, known plaintext attacks, chosen ciphertext attacks, related-key attacks, and pollution attacks. Uploading data to a single cloud service may heighten the risk of compromising confidential information. Prior research has explored several cryptography techniques like SA-EDS, RFDA, and EST to secure data storage across multiple cloud platforms. However, these existing methods exhibit vulnerabilities to various attacks. This article focuses on addressing data security challenges in multi-cloud environments and proposes an advanced approach called Proficient Security over Distributed Storage. PSDS has undergone rigorous testing against multiple attacks, demonstrating its resilience against related-key attacks, pollution attacks, chosen ciphertext attacks, and known plaintext attacks. Additionally, PSDS exhibits reduced computational time compared to existing encryption methods such as STTN and RFD.

Implementation of Digital Health Ethics: A First Step with the Adoption of 16 European Ethical Principles for Digital Health.

The potential for technology to transform health care is tremendous, but advances in digital health may also bring privacy and data security challenges that may exacerbate inequalities. Hence, it is critical that the development of digital health is included in a framework of humanistic and ethical values. France drew up its roadmap for accelerating the shift towards digital health with ethics at the forefront, along with security and interoperability pillars. Criteria such as digital health for all, transparency of data processing, trustworthy AI, and eco-responsibility and sustainability of digital health were elaborated. Under the French Presidency of the Council of the European Union, building on the proposal of ethical criteria from France, eHealth network representatives unanimously adopted 16 European ethical principles for digital health, formalizing trust commitments towards European citizens and paving the way for the European Health Data Space.

Unveiling the future: precision pharmacovigilance in the era of personalized medicine

In the era of personalized medicine, pharmacovigilance faces new challenges and opportunities, demanding a shift from traditional approaches. This article delves into the evolving landscape of drug safety monitoring in the context of personalized treatments. We aim to provide a succinct reflection on the intersection of tailored therapeutic strategies and vigilant pharmacovigilance practices. We discuss the integration of pharmacogenetics in enhancing drug safety, illustrating how genetic profiling aids in predicting drug responses and adverse reactions. Emphasizing the importance of phase IV—post-marketing surveillance, we explore the limitations of pre-marketing trials and the necessity for a comprehensive approach to drug safety. The article discusses the pivotal role of pharmacogenetics in pre-exposure risk management and the redefinition of pharmacoepidemiological methods for post-exposure surveillance. We highlight the significance of integrating patient-specific genetic profiles in creating personalized medication leaflets and the use of advanced computational methods in data analysis. Additionally, we examine the ethical, privacy, and data security challenges inherent in precision medicine, emphasizing their implications for patient consent and data management.

Research on data security in industry 4.0 manufacturing industry against the background of privacy protection challenges

ABSTRACT As the manufacturing industry advances into the era of Industry 4.0, data security has emerged as a critical concern in the face of growing privacy protection challenges. This research paper explores data security challenges in the Industry 4.0 manufacturing industry and the associated privacy protection concerns. It highlights the risks of interconnected systems and data-driven processes while emphasizing the need for a comprehensive framework. The proposed framework encompasses technological, organizational, and regulatory measures to safeguard data at all stages. The study also examines legal and ethical considerations and suggests strategies to enhance privacy protection. The research aims to provide insights for stakeholders in the manufacturing industry, policymakers, and researchers to strengthen data security practices and uphold privacy rights in the Industry 4.0 landscape.

An attribute-encryption-based cross-chain model in urban internet of vehicles

With the maturity of Internet of Vehicles (IoV) technology and blockchain technology, numerous application scenarios have emerged, including urban Internet of Vehicles, Highway Internet of Vehicles, and Specific Area Internet of Vehicles. However, the urban Internet of Vehicles systems face data security and operational mode discretization challenges. To address these issues, we propose a data security cross-chain model for the urban Internet of Vehicles. Our model utilizes hash time lock contracts to exchange data between vehicles of the same brand and model but located in different urban areas. It also uses trusted relay chains to facilitate data exchange between vehicles of the same brands but varying models within the same urban area, thus solving the problem of difficult inter-system data exchange in the urban Internet of Vehicles. In the urban Internet of Vehicles, the transmitted data encompasses business and general data, which may have varying sensitivities. To ensure data confidentiality, we propose the HR-CP-ABE algorithm, which extends CP-ABE functionalities by incorporating hierarchical decryption and revocable vehicle attributes. And the fine-grained access control by setting different access tree structures and file access validity periods. This guarantees the confidentiality and security of urban data. Finally, experimental results demonstrate that the proposed model effectively satisfies the standards for cross-chain transactions as outlined in the Evaluation Requirements for Blockchain Application Security Technology(DB43/T 1842–2020) and Evaluation Requirements for Blockchain Network Security Technology(DB43/T 1840–2020). Additionally, our model demonstrates enhanced resilience against man-in-the-middle and replay attacks.

Review of smart water management: IoT and AI in water and wastewater treatment

Integrating the Internet of Things (IoT) and Artificial Intelligence (AI) in smart water management revolutionizes sustainable water resource utilization. This comprehensive review explores these technologies' benefits, challenges, regulatory implications, and future trends. Smart water management enhances operational efficiency, predictive maintenance, and resource conservation while addressing data security and infrastructure investment challenges. Regulatory frameworks play a pivotal role in shaping the responsible deployment of AI and IoT, ensuring data privacy and ethical use. Future trends include advanced sensors, decentralized systems, quantum computing, and blockchain for enhanced water data security. The alignment with Sustainable Development Goals (SDGs) underscores the transformative potential of smart water management in achieving universal access to clean water, climate resilience, and inclusive, sustainable development. As we embrace these technologies, collaboration, public awareness, and ethical considerations will guide the evolution of intelligent and equitable water management systems.

Towards a right to repair for the Internet of Things: A review of legal and policy aspects

The way in which consumers engage with, utilise, or discard the technologies in their lives is constantly being reassessed and changed. This paper questions what role the emergent “right to repair” could play in resolving issues posed by the increasing ubiquity of the Internet of Things (IoT). The right gives consumers the ability and freedom to fix their devices, or to fair access to appropriate services that can carry out repair on their behalf. In this paper, firstly we establish the problem space surrounding consumer IoT – i.e., devices that are interconnected via the internet, enabling them to send and receive data. We reflect on hardware, software, and data components that pose legal and policy challenges for data protection, security, and sustainability. Through a literature review we then reflect on the current socio-legal developments that support or oppose changes in the consumer IoT market in regards to repair. We then highlight gaps in the existing literature that should inform future research trajectories in this area. This includes exploring disparities between environmental and consumer autonomy approaches, assessing consistency in regulatory developments, and market prioritisation. Finally, the paper concludes with a series of key insights and recommendations from our analysis including: recognition of the growing e-Waste problem and the inequalities it exacerbates and perpetuates; the need for identification and argumentation for different formulations of “repair” and how these may impact the implementation of a right going forward; the need for identification of the reasoning behind disparities in governmental approaches to the right to repair; and the need to practically translate better IoT design practices into reality.

Research on Financial Risk Management of Free Trade Port under the Revolution of Digital Economy

The commencement of the full implementation phase of Hainan's free trade port building project signifies the initiation of Hainan's endeavour to establish a free trade port with distinct Chinese attributes. The maintenance of financial security in the Hainan Free Trade Port is crucial for ensuring the stable and quick development of the port, particularly in light of the accelerated speed of financial opening in its construction. In the realm of digital finance, the concept of interconnectivity and data exchange is achieved through the transformation of prospective financial data into information chains. Digital finance has emerged as a significant component of the digital economy, exerting a substantial influence on the rebuilding of the financial ecological value system and the formation of the nascent financial services industry. Nevertheless, the expeditious advancement of digital banking has engendered a range of risk management concerns. These include the swift transmission and progression of risks inside the realm of digital finance, the escalation of data security and compliance challenges, and the augmented complexity of market regulation. This study examines the issue of digital financial risk management by assessing the current situation and trends in digital financial development. It analyses the unique characteristics of digital financial risks compared to traditional financial risks and considers the current state of financial development in Hainan. The study explores the risks that should be mitigated during the development of Hainan's free trade port and proposes measures to ensure the financial security of the port. The ultimate goal is to facilitate the sustainable development of Hainan's free trade port.

Data security challenges and its solutions in cloud computing through quantum resistant advanced encryption standards

Data security challenges and its solutions in cloud computing through quantum resistant advanced encryption standards