Security Technology Research Articles

Hypercomplex Numbers—A Tool for Enhanced Efficiency and Intelligence in Digital Signal Processing

Mathematics is the wide-ranging solid foundation of the engineering sciences which ensures their progress by providing them with its unique toolkit of rules, methods, algorithms and numerical systems. In this paper, an overview of the numerical systems that have currently found an application in engineering science and practice is offered, while also mentioning those systems that still await full and comprehensive applicability, recognition, and acknowledgment. Two possible approaches for representing hypercomplex numbers are proposed—based on real numbers and based on complex numbers. This makes it possible to justify the creation and introduction of numerical systems specifically suited to digital signal processing (DSP), which is the basis of all modern technical sciences ensuring the technological progress of mankind. Understanding the specifics, peculiarities, and properties of the large and diverse family of hypercomplex numbers is the first step towards their more comprehensive and thorough study, and hence their use in a number of high-tech intelligent applications in various engineering and scientific fields, such as information and communication technologies (ICT), communication and neural networks, cybersecurity and national security, artificial intelligence (АI), space and military technologies, industrial engineering and machine learning, astronomy, applied mathematics, quantum physics, etc. The issues discussed in this paper are, however, far from exhausting the scientific topics related to both hypercomplex numbers in general and those relevant to DSP. This is a promising scientific area, the potential of which has not yet been fully explored, but research already shows the enhanced computational efficiency and intelligent performance of hypercomplex DSP.

DTXG-RF-based Intrusion Detection System for Artificial IoT Cyber Attacks

The swift advancement of networking technology and the rising incidence of cyber-attacks have made effective cybersecurity a critical priority. The primary concern with IoT networks is their susceptibility to vulnerabilities. IoT security necessitates the substantial involvement of artificial intelligence as a security technology to mitigate these challenges. Cyberattacks are evolving in sophistication, consequently posing greater obstacles in the precise detection of intrusions. An Intrusion Detection System (IDS) is a device or software application that monitors the activities of network systems for malicious actions or policy breaches and produces reports. The primary objective of an IDS is to efficiently identify attacks. Moreover, it is imperative to identify attacks at an early stage to mitigate their effects. Machine learning models have become increasingly popular in IDSs due to their capacity to process substantial data volumes and identify patterns in real time. Machine learning involves building an algorithm to identify consistent patterns within a dataset. This study aimed to build an IDS using an ensemble machine learning (DTXG-RF) model and compare it with DT, XGBoost, KNN, RF, NB, and CatBoost on the CIC-IoT-2023 and a Ransomware dataset. The results showed that the proposed DTXG-RF outperformed other machine learning models with accuracy reaching 95.06%.

Guest Editorial Special Issue on Emerging Hardware Security and Trust Technologies—AsianHOST 2023

Guest Editorial Special Issue on Emerging Hardware Security and Trust Technologies—AsianHOST 2023

Security of differential phase shift QKD from relativistic principles

The design of quantum protocols for secure key generation poses many challenges: On the one hand, they need to be practical concerning experimental realisations. On the other hand, their theoretical description must be simple enough to allow for a security proof against all possible attacks. Often, these two requirements are in conflict with each other, and the differential phase shift (DPS) QKD protocol exemplifies these difficulties: It is designed to be implementable with current optical telecommunication technology, which, for this protocol, comes at the cost that many standard security proof techniques do not apply to it. After about 20 years since its invention, this work presents the first full security proof of DPS QKD against general attacks, including finite-size effects. The proof combines techniques from quantum information theory, quantum optics, and relativity. We first give a security proof of a QKD protocol whose security stems from relativistic constraints. We then show that security of DPS QKD can be reduced to security of the relativistic protocol. In addition, we show that coherent attacks on the DPS protocol are, in fact, stronger than collective attacks. Our results have broad implications for the development of secure and reliable quantum communication technologies, as they shed light on the range of applicability of state-of-the-art security proof techniques.

Implementation of Criminal Law in Combating Hoaxes to Build National Information Resilience that Supports Strengthening Infrastructure and Innovation (SDG 9)

Objective: This research aims to analyze the application of criminal law in the Law of the Republic of Indonesia Number 1 Year 2024 on Electronic Information and Transactions related to the eradication of hoaxes, as well as examine its impact on the development of national information security in Indonesia in accordance with SDG 9, namely National information security involves the development of a resilient and secure information and communication technology (ICT) infrastructure. Using a juridical-empirical approach, this research uses primary data through interviews with law enforcement officials and information technology experts, as well as secondary data from legal documents and literature studies. Theoretical Framework: This research draws on various concepts of criminal law, information technology, and national information security. This multidimensional approach includes legal theory, communication theory, and national resilience theory to provide a more holistic understanding of the implementation of the criminal law of the Law of the Republic of Indonesia on Electronic Information and Transactions related to the eradication of hoaxes in the eradication of hoaxes and their impact on information resilience in Indonesia. Method: This research uses Qualitative data with a Normative Juridical Approach, the type of research is analytical to evaluate the effectiveness of the implementation of the Electronic Information and Transaction Law in eradicating hoaxes, secondary data including laws and regulations, academic literature and scientific journals related to criminal law, hoaxes, and national information security. Data collection techniques are literature studies, interviews, observations, and concrete case studies of the spread of hoaxes that are handled using the Law of the Republic of Indonesia Number 1 of 2024 concerning Information and Electronic Transactions. Results and Discussion: The implementation of the Criminal Law of the Republic of Indonesia Law No. 1 of 2024 on Electronic Information and Transactions against Hoax Eradication is a significant legal instrument in dealing with the spread of hoaxes in Indonesia. National information security is highly dependent on people's digital literacy, the speed of government response, and cooperation with digital platforms in tackling the spread of false information. Efforts and strategies are needed to revise the Law of the Republic of Indonesia No. 1 of 2024 on Electronic Information and Transactions to clarify the definition of articles that have multiple interpretations and provide clearer implementation guidelines for law enforcement officials. Research Implications: Theoretical Implications i.e. These findings expand the understanding of the relationship between the implementation of criminal law and the concept of national information security, so that it can serve as a foundation for further research in the field of cyber law and digital literacy. Practical Implications: Law enforcement officials can use the findings of this study as a reference to improve the approach to investigation, legal proof, and the application of sanctions against hoax perpetrators. Originality/Value: This research makes a unique contribution by deeply evaluating the challenges of multiple interpretations in certain articles of Law of the Republic of Indonesia Number 1 Year 2024 on Electronic Information and Transactions and their impact on the effectiveness of law enforcement, which has not been widely discussed in previous studies.

What technologies are used to ensure information security and data protection at enterprises and organizations?

The article is a comprehensive review of the main aspects of ensuring information security in modern organizations. The author considers key organizational and technical measures to protect information, paying special attention to the human factor and personnel training. The issues of password management, timely system updates, security audits, the use of modern security technologies and domestic solutions are discussed. Legal aspects and features of ensuring information security in various industries are touched upon. The article contains practical recommendations for managers on increasing the level of protection of the company's information assets.

Advancing Asset Tokenization in the European Union and Latvia: A Regulatory and Policy Perspective

Our study examines the regulatory challenges and opportunities of asset tokenization within the context of the European Union (EU), emphasizing the balance between technological innovation and investor protection in the digital economy. Focusing on 2023 EU Markets in Crypto-Assets Regulation and its application in Latvia, we utilize comparative legal and integrative literature review methodologies to explore how regulatory frameworks can enhance investor accessibility, liquidity, and transparency in digital transactions. Our findings emphasize the importance of strong legal frameworks in promoting economic growth and protecting investors, thereby contributing to a more inclusive financial ecosystem. By examining the regulatory landscape for distributed ledger technology, we provide insights into how regulations can balance innovation in asset management with the imperative of investor protection. We offer a broad analysis of the intersection between legal frameworks and technological advancements in Latvia, illustrating how diverse regulatory approaches can support both economic development and investor interests. Our research originality lies in its focus on the EU’s regulatory diversity, particularly in Latvia, and its implications for broader European and international regulatory environments. Our study contributes to ongoing discussions on optimizing regulatory strategies to facilitate secure and advantageous financial technologies, reflecting the diversity of legal and economic approaches across Europe.

EFFECTIVENESS OF MULTIFACTOR AUTHENTICATION TECHNOLOGY FOR PROTECTING STUDENT PRIVACY : A SYSTEMATIC LITERATURE REVIEW

Student privacy is a major concern in the digital era, especially with the increasing use of educational technologies such as Learning Management Systems (LMS) and IoT devices. Traditional authentication systems are considered inadequate in the face of complex security threats. Therefore, this study evaluates the effectiveness of Multi-Factor Authentication (MFA) in protecting student privacy in an educational environment. This study uses a Systematic Literature Review (SLR) design based on 86 relevant articles selected from Scopus indexed journals (2020–2024). The articles were analyzed thematically using PRISMA Flow Diagram to identify patterns, trends, and gaps related to MFA implementation. The findings show that MFA can improve student privacy protection by reducing the risk of data leaks by up to 80%, lowering unauthorized access, and increasing user trust. Key technologies include biometrics, OTP, and blockchain, which have proven to be effective in improving security. However, MFA implementation faces obstacles such as cost, system complexity, and biometric data privacy concerns. This research makes an important contribution to academic literature and educational data security practices. Recommendations for further research include the exploration of behavior-based authentication methods, studies on user perception, and the implementation of MFA in educational environments with limited infrastructure. The practical implications include strengthening technology security policies in educational institutions and educating users to raise awareness of the importance of data privacy protection. Keywords: Student privacy, Multi-Factor Authentication (MFA), Educational Data Security, Biometric Technology

Identifying Upper d-Band Edge as Activity Descriptor for Ammonia Oxidation on PtCo Alloys in Low-Temperature Direct Ammonia Fuel Cells.

Low-temperature direct ammonia fuel cell (DAFC) stands out as a more secure technology than the hydrogen fuel cell system, while there is still a lack of elegant bottom-up synthesis procedures for efficient ammonia oxidation reaction (AOR) electrocatalysts. The widely accepted d-band center, even with consideration of the d-band width, usually fails to describe variations in AOR reactivity in many practical conditions, and a more accurate activity descriptor is necessary for a less empirical synthesis path. Herein, the upper d-band edge, εu, derived from the d-band model, is identified as an effective descriptor for accurately establishing the descriptor-activity relationship. Using the PtCo alloy with varying atomic composition as an example, the εu value succeeds in reflecting the corresponding trends of AOR activity, showing striking linear correlation with a coefficient of determination (R2) as high as 0.90. The effectiveness of the established descriptor-activity relationship is verified experimentally. The optimum electrocatalyst delivers an excellent peak current density of 74.04 A g-1 at 5 mV s-1, and the assembled DAFC generates a high power density, outperforming the majority of the extensively reported systems. This work brings fundamental insights into the relationship between chemical reactivity and electronic structure and benefits rational optimization of AOR electrocatalyst for next-generation low-temperature DAFC.

Adoption deep learning approach using realistic synthetic data for enhancing network intrusion detection in intelligent vehicle systems

In the dynamic field of cybersecurity within intelligent vehicle systems, the sophistication of threats necessitates continual advancements in security technologies. Traditional Network Intrusion Detection Systems (NIDS) often fall short in detecting emerging and sophisticated intrusion methods, primarily due to their reliance on static datasets that fail to capture the nuanced dynamics and complexity of modern network intrusions. This study presents a sophisticated simulation for NIDS tailored to intelligent vehicle environments, utilizing the extensive capabilities of Scapy—a robust network manipulation tool—to generate a highly accurate dataset of network traffic reflective of real-world scenarios. We created a diverse dataset involving 100,000 network flows, covering a wide array of benign, malicious, and anomalous traffic patterns, to thoroughly evaluate the detection capabilities of our proposed system. This dataset was analyzed using a deep learning framework employing a Convolutional Neural Network (CNN), which demonstrated outstanding performance metrics: an accuracy of 99.08%, precision of 98.96%, recall of 99.11%, and an F1 score of 99.03%. These metrics showcase the system's enhanced capability to precisely classify various network flows, emphasizing the importance of realistic synthetic data in boosting the training and accuracy of NIDS in intelligent vehicles. The results of this research are significant, marking a step forward towards more flexible and preemptive security measures for intelligent vehicles, and effectively narrowing the gap between simulation-based testing and real-world network environments.

Secure privacy-preserving record linkage system from re-identification attack.

Privacy-preserving record linkage (PPRL) technology, crucial for linking records across datasets while maintaining privacy, is susceptible to graph-based re-identification attacks. These attacks compromise privacy and pose significant risks, such as identity theft and financial fraud. This study proposes a zero-relationship encoding scheme that minimizes the linkage between source and encoded records to enhance PPRL systems' resistance to re-identification attacks. Our method's efficacy was validated through simulations on the Titanic and North Carolina Voter Records (NCVR) datasets, demonstrating a substantial reduction in re-identification rates. Security analysis confirms that our zero-relationship encoding effectively preserves privacy against graph-based re-identification threats, improving PPRL technology's security.

Application of blockchain technology in student learning data security and privacy protection

Application of blockchain technology in student learning data security and privacy protection

Advanced Security Measures for Virtualized Data Storage: A Comprehensive Study

With the widespread application of virtualization technology in data centers, the security issues of data storage have become increasingly prominent. This paper aims to explore the application of information security in virtualized data storage to address the increasingly complex security challenges. The article first reviews the development of virtualization technology and the key technologies of virtualized data storage, then analyzes the current state and threats faced by information security in virtualized environments. Based on this, the paper discusses security requirements such as data integrity, access control, and privacy protection in detail, and delves into the application of information security technologies such as data encryption, intrusion detection and defense systems, security information and event management, and virtualization security policy management. Furthermore, this paper proposes a comprehensive security framework for virtualized data storage and describes its design principles, component architecture, and implementation deployment strategies in detail. Through experimental design and case studies, the paper validates the effectiveness of the proposed framework and evaluates its performance. Finally, the paper summarizes the research findings and proposes prospects for future research directions. This study not only provides theoretical support and practical guidance for information security in virtualized data storage but also offers valuable references for researchers and practitioners in related fields.

Keamanan Informasi dan Privasi Pengguna dalam Layanan Perpustakaan Digital

The development of libraries from conventional to digital-based libraries has brought significant impacts on the way library services are provided and accessed by users. However, along with its benefits, digital libraries face new challenges related to data security and user privacy such as effective protection of personal data. This research aims to analyze security and privacy threats in digital library services and identify the implementation of security technologies and privacy policies in this context. This research uses the narrative literature review method to analyze 12 references relevant to the topic of information security and user privacy in digital library services. This research has security and privacy risks involved in digital library services, how security technologies are implemented, and how privacy regulations and policies impact the situation. Results show that virus attacks and brute force attacks are some of the security threats of digital libraries. To protect sensitive user data, the use of security technologies such as data encryption and the use of firewalls are also very important. Privacy regulations and policies such as GDPR go a long way in helping digital libraries maintain the privacy of their users. This analysis enhances our understanding of the problems and solutions in data security management and user privacy in digital library services. Thus, this research highlights the importance of managing information security and user privacy in the context of digital library services to ensure optimal protection of sensitive user data.

Optimizing PoS agent banking for inclusive rural economies:

Purpose: This paper explores the impact of Point-of-Sale (PoS) agent banking on streamlining digital and cash transactions, emphasizing its potential to alleviate financial exclusion in rural South African communities while addressing inherent challenges. Method: An exploratory qualitative case study research methodology was employed, utilizing a participatory community-led action research approach. With proponents from the Routine Activity Theory, this study provides a comprehensive understanding of potential criminalities resonating with the model’s implementation. Findings: In South Africa, PoS banking holds significant potential to stimulate entrepreneurship and improve livelihoods in underserved rural areas. However, the success of PoS banking is hindered by cybersecurity risks, fraud, agent credibility issues, and physical security threats like robbery. Possible interventions include strengthening agent registration, raising public awareness, enhancing digital security protocols, and bolstering regulatory and law enforcement frameworks. Significance: The research highlights the importance of PoS banking in overcoming financial exclusion and fostering community development in Africa. It emphasizes the necessity for a multi-faceted approach to mitigate associated risks, combining better regulatory oversight, law enforcement, public education, and technological security. These measures are essential to ensure the safe and sustainable growth of PoS banking in promoting economic inclusion and resilience in vulnerable communities. Further inquiry that provides intervention measures to other forms of implementation storms is critically imperative in fostering its integration propinquity.

Precision Agriculture Technology Innovation in Supporting Food Security in the Era of Industrial Revolution 4.0

The Industrial Revolution 4.0 has driven transformative changes in agriculture through the adoption of precision agriculture (PA) technologies, aimed at optimizing resource use and enhancing food security. This study conducts a systematic literature review of 52 Scopus-indexed documents to evaluate the role of these technologies in addressing global food challenges. Key innovations such as IoT, AI, drones, and robotics are highlighted for their contributions to increasing agricultural productivity, ensuring environmental sustainability, and mitigating the effects of climate variability. Despite their potential, barriers such as high implementation costs, technological complexity, and limited access in developing regions hinder widespread adoption. The findings underscore the need for targeted policies, infrastructure development, and inclusive practices to harness PA technologies for food security fully. This study provides valuable insights for researchers, policymakers, and practitioners to foster sustainable agricultural systems.

Examining IoT Applications in the Perspective of Security Technologies in 5G Networks

While 5G technology revolutionizes the communication infrastructure as the driving force of digital transformation, it also brings security threats. Offering the advantages of high-speed data transmission and low latency, this technology has created new attack surfaces for malicious actors with complex waveforms and wide-band communication channels. Increasing device connections and network complexity reveal the need for stronger security solutions against cyberattacks that threaten the privacy of individuals and institutions. Overall, 5G is also leading to advances in various sectors by improving connectivity and performance. The 5G security architecture offers a variety of advanced concepts and techniques to address the unique challenges posed by this technology. This study examines the basic concepts and protection techniques that are integral to 5G security, as well as the security framework of the technology that addresses the increasing complexity and scale of modern networks and provides a secure and resilient communication environment.

Analysis of Technology Leakage Threats in Industrial Settings Using Generative AI and Strategies for Technological Security

Analysis of Technology Leakage Threats in Industrial Settings Using Generative AI and Strategies for Technological Security

Modern legislation regarding the challenges of 21st century technology - selected aspects of legal regulations and technology security

This publication discusses legal issues related to selected areas of security, including cybersecurity, digital service security, fire safety as well as safety in the area of nanotechnology – in particular in relation to requirements concerning environmental standards, products and waste management (storage, recycling, incineration, etc.). The authors of the article analyse not only the applicable EU and national regulations, indicating the proposed areas of change, but also discuss the threats related to the use of 21st century technologies and indicate the challenges posed by new technologies in the regulatory area. By analysing extremely different substantive issues covering law, engineering and science, the authors made the same assessment of the relations between law and 21st century technology. The results of research and analyses presented by them concerning both cybersecurity and fire safety and the use of nanotechnology confirm the thesis that law is unable to keep up with the dynamic development of new technologies.

Detection of Digital Currency Fraud through a Distributed Database Approach and Machine Learning Model

The world is witnessing a noticeable increase in financial exchange in digital currencies such as Bitcoin, Ethereum, and others, as transactions in electronic markets have begun to rise recently, which increases the difficulty of maintaining security and trust in decentralized financial systems that use distributed databases and the technologies that interact with them in Ethereum networks, blockchain, etc. This study presents a hybrid model based on the PyCaret library and includes 12 machine learning classifiers, with the aim of identifying fraudulent activities in Bitcoin transactions and enhancing the security of Ethereum networks and blockchain technology. The results reveal the effectiveness of different models in identifying fraudulent activities on the Ethereum network through a comprehensive performance comparison. The classifiers that showed the highest accuracy scores, which ranged from 0.9814 to 0.9862, were the Random Forest classifier, the visual gradient boosting machine, and the additive tree classifier. It is important to note that both Gradient Boosting Classifier and K Neighbors Classifier performed well, with accuracies above 0.96 and AUC scores above 0.99. However, some models, such as Naive Bayes, showed lower accuracy and AUC scores, suggesting that they have limitations in terms of accurately detecting fraudulent transactions. These results highlight the importance of choosing appropriate machine learning models for fraud detection tasks in general, with ensemble techniques such as Extra Trees and Random Forest showing great promise in this regard.