Information Security Solutions Research Articles

Quantum Physical Unclonable Function Based on Multidimensional Fingerprint Features of Single Photon Emitters in Random AlN Nanocrystals

AbstractPhysical unclonable function (PUF) has emerged as a unique physical'fingerprint' that is inherently difficult to replicate. It shows tremendous application value in various hardware security areas such as identity authentication, chip anticounterfeiting, communication encryption, blockchain, etc. However, with the rapid development of 3D nanoprinting, classical PUFs constructed with disordered micro‐nanostructures face tremendous threats from physical cloning attacks. Herein, this study proposes and demonstrates the utilization of room‐temperature single‐photon emitters derived from atomic defects in randomly distributed pyramidal aluminum nitride (AlN) nanocrystals as a novel quantum PUF to resist physical cloning attacks. The fabrication of this quantum PUF on silicon (Si) wafers enables seamless integration with silicon photonic integrated circuits. The multidimensional fingerprint features of the single‐photon emitters are highly sensitive to the lattice parameters of the uneven AlN nanocrystals. Furthermore, each single photon emitter can work as a quantum random number generator to ensure the fundamental unpredictability of PUFs. The subatomic precision requirement coupled with unpredictable quantum emission behavior makes it practically impossible to attack the proposed quantum PUF, providing a promising solution for information security in the post‐quantum era.

Traceable Optical Physical Unclonable Functions Based on Germanium Vacancy in Diamonds.

Physical unclonable functions (PUFs) have emerged as an unprecedented solution for modern information security and anticounterfeiting by virtue of their inherent unclonable nature derived from distinctive, randomly generated physical patterns that defy replication. However, the creation of traceable optical PUF tags remains a formidable challenge. Here, we demonstrate a traceable PUF system whose unclonability arises from the random distribution of diamonds and the random intensity of the narrow emission from germanium vacancies (GeV) within the diamonds. Tamper-resistant PUF labels can be manufactured on diverse and intricate structural surfaces by blending diamond particles into polydimethylsiloxane (PDMS) and strategically depositing them onto the surface of objects. The resulting PUF codes exhibit essentially perfect uniformity, uniqueness, reproducibility, and substantial encoding capacity, making them applicable as a private key to fulfill the customization demands of circulating commodities. Through integration of a digitized "challenge-response" protocol, a traceable and highly secure PUF system can be established, which is seamlessly compatible with contemporary digital information technology. Thus, the GeV-PUF system holds significant promise for applications in data security and blockchain anticounterfeiting, providing robust and adaptive solutions to address the dynamic demands of these domains.

Information-hiding cameras: Optical concealment of object information into ordinary images.

We introduce an information-hiding camera integrated with an electronic decoder that is jointly optimized through deep learning. This system uses a diffractive optical processor, which transforms and hides input images into ordinary-looking patterns that deceive/mislead observers. This information-hiding transformation is valid for infinitely many combinations of secret messages, transformed into ordinary-looking output images through passive light-matter interactions within the diffractive processor. By processing these output patterns, an electronic decoder network accurately reconstructs the original information hidden within the deceptive output. We demonstrated our approach by designing information-hiding diffractive cameras operating under various lighting conditions and noise levels, showing their robustness. We further extended this framework to multispectral operation, allowing the concealment and decoding of multiple images at different wavelengths, performed simultaneously. The feasibility of our framework was also validated experimentally using terahertz radiation. This optical encoder-electronic decoder-based codesign provides a high speed and energy efficient information-hiding camera, offering a powerful solution for visual information security.

An Information Security Solution for Vehicle-to-Grid Scheduling by Distributed Edge Computing and Federated Deep Learning

An Information Security Solution for Vehicle-to-Grid Scheduling by Distributed Edge Computing and Federated Deep Learning

Addressing the gap in information security: an HR-centric and AI-driven framework for mitigating insider threats

PurposeDespite ongoing reports of insider-driven leakage of confidential data, both academic scholars and practitioners tend to focus on external threats and favour information technology (IT)-centric solutions to secure and strengthen their information security ecosystem. Unfortunately, they pay little attention to human resource management (HRM) solutions. This paper aims to address this gap and proposes an actionable human resource (HR)-centric and artificial intelligence (AI)-driven framework.Design/methodology/approachThe paper highlights the dangers posed by insider threats and presents key findings from a Leximancer-based analysis of a rapid literature review on the role, nature and contribution of HRM for information security, especially in addressing insider threats. The study also discusses the limitations of these solutions and proposes an HR-in-the-loop model, driven by AI and machine learning to mitigate these limitations.FindingsThe paper argues that AI promises to offer many HRM-centric opportunities to fortify the information security architecture if used strategically and intelligently. The HR-in-the-loop model can ensure that the human factors are considered when designing information security solutions. By combining AI and machine learning with human expertise, this model can provide an effective and comprehensive approach to addressing insider threats.Originality/valueThe paper fills the research gap on the critical role of HR in securing and strengthening information security. It makes further contribution in identifying the limitations of HRM solutions in info security and how AI and machine learning can be leveraged to address these limitations to some extent.

Irreversible Ca2B2O5‐Based Multicolor Mechanoluminescence for Security Labels

AbstractAs the Internet of Things (IoT) continues its pervasive influence and digital technologies increasingly weave into the fabric of daily lives, the imperative for robust security measures becomes paramount to mitigate the escalating risks to the digital landscape. Traditional anti‐counterfeiting technologies, while capable to some extent, are constrained by factors such as material toxicity, low‐security efficacy, and intricate fabrication process. Mechanoluminescent materials have shown great potential in the areas of anti‐counterfeiting and security. Here, a multicolor mechanoluminescent (ML) material (lanthanide doped Ca2B2O5) synthesized by solid‐phase reaction is reported for the first time at a relatively lower temperature of 750 °C. The ML devices are obtained by embedding ML materials into polydimethylsiloxane elastomer, allowing for stress visualization with colors ranging from blue and green to orange and red. Through elaborate co‐doping, successive and accurate stress visualizations are realized. Finally, security labels based on the lanthanide ions doped Ca2B2O5 are prepared and also demonstrated to safeguard valuable packages. This work provides a novel multicolor ML material and pioneers its use in security labels, offering a universal solution for multicolor display, information security, dynamic anti‐counterfeiting, and other potential applications within the realm of IoT.

Analysis on the Thinking Innovation of Ideological and Political Education Based on the Theory of Blockchain in the Information Age

Abstract Blockchain is the systematic integration innovation of distributed accounting, consensus mechanisms, point-to-point transmission, encryption algorithms, intelligent contracts and other technologies, providing a solution for information security and social trust in the Information Age. The principles, characteristics and deep thinking of Blockchain technology can promote thinking innovation and solve the existing obstacles, including the weak effectiveness of education activities, the burnout of education subjects and the lack of mutual trust in the field of ideological and political education. This paper focuses on analyzing the expanded application of Blockchain theory in ideological and political education. The fit of blockchain technology application of educational thinking innovation and value orientation is 0.928; there is a linear relationship, and after applying blockchain technology for one year, the difference between the students’ thinking tendencies and the six aspects of their performance, except for the desire for knowledge, is statistically significant (P<0.5) when compared with the situation of the students one year ago. In addition, it is emphasized that we should build Learn Ledger, promote the participation degree of education subjects, establish a new interactive trust mechanism and intensify endogenous incentives to promote the deep transformation of ideological and political education, enhance the effectiveness of educational content, expand the participation in educational activities, and strengthen the multi-subject trust-relationship reconstruction of ideological and political education.

Information Security and Telecommunications Prospects of Machine-Learning-Based Methods in Chaotic Systems

In the dynamic landscape of information security and telecommunications, this paper delves into the multifaceted realm of machine-learning-based methods, with a particular focus on their application in chaotic systems. An informative introduction sets the way for a thorough examination of the major benefits provided by reservoir computing (RC) and machine learning (ML) in telecommunications. The first segment of this study scrutinizes the role of machine learning in fortifying information security. With the ever-evolving nature of cyber threats, understanding the nuances of ML becomes imperative. The article highlights key advancements and features in ML that contribute to bolstering data security, providing a nuanced perspective on its efficacy in addressing the intricate challenges posed by contemporary paradigms for information security. Moving forward, the discussion expands to reservoir computing and its implications in telecommunications. Reservoir computing, with its unique approach to processing information through dynamic systems, has emerged as a promising technique. The article dissects its applications in the telecommunications sector, shedding light on how reservoir computing augments information processing and transmission efficiency within complex networks. A pivotal aspect of this paper is the exploration of the double-reservoir solution — a cutting-edge approach that combines the strengths of reservoir computing for enhanced performance. This innovative solution is dissected in detail, uncovering its prospects and the challenges it presents. The incorporation of double-reservoir solutions into chaotic systems represents a paradigm shift in the optimization of system dynamics and represents a major advancement in tackling important telecommunications difficulties. Yet not just this paper offers insights into this solution, it fairly describes possible challenges with implementation of such a model. It is to be taken into consideration, hence there is no ‘perfect’ solution for such a complex problem. This paper provides a comprehensive view of machine-learning-based solutions for information security and telecommunications challenges. By unraveling the capabilities of both machine learning and reservoir computing, it unlocks avenues for further research and development in harnessing these technologies to fortify the foundations of secure and efficient telecommunications in the face of constantly developing threats. The insights presented herein lay the groundwork for future innovations, urging researchers and practitioners to delve deeper into the synergy of machine learning and chaotic systems for transformative advancements in these critical domains.

Mathematical Model of Cyber Risks Management Based on the Expansion of Piecewise Continuous Analytical Approximation Functions of Cyber Attacks in the Fourier Series

The comprehensive system of information security of an enterprise includes both tactical aspects of information and strategic priorities, reflecting the information policy and information strategy of the enterprise. Ensuring a given level of cybersecurity requires the identification of threat actors, their purpose, intentions of attacks on the IT infrastructure, and weak points of the enterprise’s information security. To achieve these goals, enterprises need new information security solutions. In this work, a mathematical model of the process of cyber risk management in the enterprise, which is based on the distribution of piecewise continuous analytical approximating functions of cyber attacks in the Fourier series, is obtained. A constant continuous monitoring and conduction of cyber regulatory control of the enterprise on time makes it possible to effectively ensure the cybersecurity of the enterprise in real time—predicting the emergence of cyber threats to some extent—which, in turn, determines the management of cyber risks arising in the field of information security of the enterprise. Such a Fourier series expansion of the piecewise continuous analytical approximating function of the intensity of cyber attacks on damage to standard software, obtained by approximating empirical–statistical slices of the intensity of cyber attacks on damage to standard software for each time period by analytical functions, opens up new mathematical possibilities of transition to systems of regulatory control of cyber threats of the enterprise from discrete to continuous automated process for such types of control.

Evaluation of Blockchain Networks’ Scalability Limitations in Low-Powered Internet of Things (IoT) Sensor Networks

With the development of Internet of Things (IoT) technologies, industries such as healthcare have started using low-powered sensor-based devices. Because IoT devices are typically low-powered, they are susceptible to cyber intrusions. As an emerging information security solution, blockchain technology has considerable potential for protecting low-powered IoT end devices. Blockchain technology provides promising security features such as cryptography, hash functions, time stamps, and a distributed ledger function. Therefore, blockchain technology can be a robust security technology for securing IoT low-powered devices. However, the integration of blockchain and IoT technologies raises a number of research questions. Scalability is one of the most significant. Blockchain’ scalability of low-powered sensor networks needs to be evaluated to identify the practical application of both technologies in low-powered sensor networks. In this paper, we analyse the scalability limitations of three commonly used blockchain algorithms running on low-powered single-board computers communicating in a wireless sensor network. We assess the scalability limitations of three blockchain networks as we increase the number of nodes. Our analysis shows considerable scalability variations between three blockchain networks. The results indicate that some blockchain networks can have over 800 ms network latency and some blockchain networks may use a bandwidth over 1600 Kbps. This work will contribute to developing efficient blockchain-based IoT sensor networks.

Evaluation of Correlation between Temperature of IoT Microcontroller Devices and Blockchain Energy Consumption in Wireless Sensor Networks.

Blockchain technology is an information security solution that operates on a distributed ledger system. Blockchain technology has considerable potential for securing Internet of Things (IoT) low-powered devices. However, the integration of IoT and blockchain technologies raises a number of research issues. One of the most important is the energy consumption of different blockchain algorithms. Because IoT devices are typically low-powered battery-powered devices, the energy consumption of any blockchain node must be kept low. IoT end nodes are typically low-powered devices expected to survive for extended periods without battery replacement. Energy consumption of blockchain algorithms is an important consideration in any application that combines both technologies, as some blockchain algorithms are infeasible because they consume large amounts of energy, causing the IoT device to reach high temperatures and potentially damaging the hardware; they are also a possible fire hazard. In this paper, we examine the temperatures reached in devices used to process blockchain algorithms, and the energy consumption of three commonly used blockchain algorithms running on low-powered microcontrollers communicating in a wireless sensor network. We found temperatures of IoT devices and energy consumption were highly correlated with the temperatures reached. The results indicate that device temperatures reached 80 °C. This work will contribute to developing energy-efficient blockchain-based IoT sensor networks.

A look at the prospects for the development of the market of domestic information security solutions

We are in a state where the information security industry of all economic entities and authorities of our country has faced enormous challenges. We are forced to resist foreign hacker groups and protect our digital economy from their attacks. I am convinced that in the end we will cope, and the domestic information security industry will come out of this confrontation much stronger and more stable. The article provides a forecast for the accelerated development of Russian information security solutions, and substantiates the assumption that the basis of this development will be the maximum use of artificial intelligence.

PRACTICAL APPROACHES TO ORGANIZING SECURE DATA TRANSFER VIA TLS PROTOCOL USING OPENSSL MEANS

Information protection during message transmission is one of the most important tasks in the modern world. The workstations of a particular organization can be well protected using different software and hardware means, but when information is transferred to an open unprotected space, there is a high probability of data leakage, interception, and substitution. In most cases, the use of insufficiently effective security measures causes the loss of personal data of citizens, data of an enterprise or organization that is a commercial secret, information with limited access or even state secrets of the critical infrastructure sector. In this case, it is considered appropriate to use modern cryptographic methods to organize secure data transmission. Considering the ever-growing statistics of cyberattacks on information and telecommunication networks, after in-depth analysis and development of this issue, the authors of the article highlighted the current directions of protection of information and telecommunication networks and security solutions in information and telecommunication networks. The authors have comprehensively covered and investigated the basic principles of the modern state of data transmission security and the organization of information protection during its transmission using the TLS protocol, which made it possible to determine the directions for solving or modernizing existing information security means. Also, attention is paid to the development of a software implementation of the module for secure data transmission in the information and telecommunications network using the improved TLS protocol by means of OpenSSL, which made it possible to establish connections using digital signature algorithms. The authors are planning a series of scientific and technical solutions for the development and implementation of effective cryptographic methods to ensure the security of information and telecommunications networks

ICT as a Global Solution for Patients Information Security

ICT as a Global Solution for Patients Information Security

Automation of the process of responding to information security incidents of the organization

The article discusses the process of automating the response to information security incidents of the organization. The main stages of automation planning, key points and a sequence of actions for developing a correct response scenario for a certain type of incident are recommended. As an example, the process of building a scenario for responding to a "phishing" incident in accordance with the set of information security solutions defined for the example, which may be available in the organization, is given. The corresponding steps of the response scenario are described in text, plan, and graphic format. A graphic example of the implementation of the developed plan in the SOAR class system is given.

Authorship Attribution of Social Media and Literary Russian-Language Texts Using Machine Learning Methods and Feature Selection

Authorship attribution is one of the important fields of natural language processing (NLP). Its popularity is due to the relevance of implementing solutions for information security, as well as copyright protection, various linguistic studies, in particular, researches of social networks. The article is a continuation of the series of studies aimed at the identification of the Russian-language text’s author and reducing the required text volume. The focus of the study was aimed at the attribution of textual data created as a product of human online activity. The effectiveness of the models was evaluated on the two Russian-language datasets: literary texts and short comments from users of social networks. Classical machine learning (ML) algorithms, popular neural networks (NN) architectures, and their hybrids, including convolutional neural network (CNN), networks with long short-term memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT), and fastText, that have not been used in previous studies, were applied to solve the problem. A particular experiment was devoted to the selection of informative features using genetic algorithms (GA) and evaluation of the classifier trained on the optimal feature space. Using fastText or a combination of support vector machine (SVM) with GA reduced the time costs by half in comparison with deep NNs with comparable accuracy. The average accuracy for literary texts was 80.4% using SVM combined with GA, 82.3% using deep NNs, and 82.1% using fastText. For social media comments, results were 66.3%, 73.2%, and 68.1%, respectively.

Information Security Problems and Solutions in Cloud Era

Era of cloud computing, data and personal information into gold on the “cloud” has great value, at present for the cloud of the era of corporate and personal data leakage, network attacks and other security events are occurring, this article mainly discusses the cloud age how to ensure the security of enterprise and personal data, the information security how from passive defense to conduct surveillance and response, etc are analyzed and explored.

Implementation Of Encryption And Hidden Data Using Tiny Encryption Algorithm And End Of File

Security and confidentiality of data is one important aspect of an information system. The information can be misused very large losses in high-profile cases such as vital information confidential corporate, customer data banks and etc. Information security solutions in one of them can be used with cryptography. Cryptographic algorithms used in this study is a tiny encryption algorithm. Cryptographic data security attacks can always wear can occur, with this in mind the authors added security techniques to perform data hiding with the media as a placeholder, this term is called steganography. Steganography is used in this study is the end of the file. These techniques make the process of data hiding which is located at the end of the image, so it does not affect the image quality of the reservoir. In this study, a system built using microsoft visual studio 2010 C#. This system can work well, but has a color image blur caused to the container caused by the inserted message, where the greater the size of the message was inserted then color the image blur that arises will be many more.

Consumers’ attitudes toward information security threats against connected and autonomous vehicles

Connected and autonomous vehicles (CAVs) and intelligent transport systems are transforming individual driving behavior and thus, the vehicle industry and transport sector. However, increasing vehicle connectivity makes CAVs more susceptible to cyber-attacks, which is a barrier to consumers’ CAV adoption. This study analyzes the types of information security threats consumers consider to be the most dangerous and consumer preference for the information security solution that protects their CAVs from such information security threats. We use stated preference data from a discrete choice experiment and a mixed logit model to reflect consumers’ heterogeneity on information security threats and solutions. Furthermore, we investigate the effects of experiencing privacy leakage on attitudes toward CAV information security threats by dividing respondents into two groups. The results show that consumers regard communication failure and the unauthorized collection of personal information as dangerous information security threats, which implies that confidentiality and availability are more essential to CAV security than other factors. Moreover, convenience of use, such as having automatic updates and a security dashboard, is also important when purchasing a CAV information security solution. We also find that respondents who have experienced privacy leakage have a higher preference for protecting CAVs from information security threats than those who have not.

IMPACT OF CYBER SECURITY COST ON THE FINANCIAL PERFORMANCE OF E-BANKING: MEDIATING INFLUENCE OF PRODUCT INNOVATION PERFORMANCE

Purpose of Study: The study objectives are to: a) conduct a meta-analytical review to analyze the association among cybersecurity costs, such as prevention and detection costs (PDC), response costs (RC), development costs (DC), and indirect costs (IC), on the e-banking product innovation performance (PIP) and financial performance (FP), b) to evaluate the causal association of cyber security costs, i.e., (PDC, RC, DC, and IC) on PIP and FP; and c) investigate the mediating effects of PIP in a relationship between PDC, RC, DC, IC and FP. 
 Methodology: The study sample was the managerial cadre employees of various electronic banks (e-banks) working in Pakistan. The survey was conducted by distributing the questionnaire among the employees of e-banks working in Pakistan. The collected data were estimated via multivariate statistical techniques.
 Findings: The results of the study showed that a) the costs associated with cybersecurity, specifically PDC, RC, and DC, have a statistically significant effect on PIP and e-banking FP, whereas IC has a negative significant influence on the PIP and FP, b) the PIP has a statistically significant effect on e-banking FP, and c) the PIP partially mediates an association between PDC, RC, DC, and FP, whereas, PIP insignificantly mediates in a relationship amongst IC and e-banking FP. 
 Application of Study: The study will applicable in the modern electronic banking (e-banking) systematic risk control and information security solution.
 Novelty: The study is novel in the context of cyber security costs, including (PDC, RC, DC, IC) by measuring its influence on PI and e-banking FP.