Security Research Research Articles

What goes around comes around: an in-depth analysis of how respondents interpret ISP non-/compliance questionnaire items

Purpose Research on employee non-/compliance to information security policies suffers from inconsistent results and there is an ongoing discussion about the dominating survey research methodology and its potential effect on these results. This study aims to add to this discussion by investigating discrepancies between what the authors claim to measure (theoretical properties of variables) and what they actually measure (respondents’ interpretations of the operationalized variables). This study asks: How well do respondents’ interpretations of variables correspond to their theoretical definitions? What are the characteristics of any discrepancies between variable definitions and respondent interpretations? Design/methodology/approach This study is based on in-depth interviews with 17 respondents from the Swedish public sector to understand how they interpret questionnaire measurement items operationalizing the variables Perceived Severity from Protection Motivation Theory and Attitude from Theory of Planned Behavior. Findings The authors found that respondents’ interpretations in many cases differ substantially from the theoretical definitions. Overall, the authors found four principal ways in which respondents interpreted measurement items – referred to as property contextualization, extension, alteration and oscillation – each implying more or less (dis)alignment with the intended theoretical properties of the two variables examined. Originality/value The qualitative method used proved vital to better understand respondents’ interpretations which, in turn, is key for improving self-reporting measurement instruments. To the best of the authors’ knowledge, this study is a first step toward understanding how precise and uniform definitions of variables’ theoretical properties can be operationalized into effective measurement items.

ТЕОРЕТИКО-МЕТОДОЛОГІЧНІ ПІДХОДИ ДО ВИЗНАЧЕННЯ РІВНЯ ЕНЕРГЕТИЧНОЇ БЕЗПЕКИ УКРАЇНИ

The article deals with theoretical and methodological approaches to determining the level of energy security, which is one of the most important components of the national security of the state. The evolution of theoretical foundations of energy security research is considered, and the definition of energy security is generalized. Based on the analysis of the main provisions of the Energy Security Strategy, it is determined that the Strategy needs to be revised and improved in connection with the military aggression of the Russian Federation against Ukraine. In order to determine the level of energy security of Ukraine, the article considers a universal methodology of identification and strategizing, which compares the state of different spheres and substantiates strategic scenarios of security development. The system of indicators for determining the level of energy security of Ukraine, which includes a reduced set of 48 indicators, divided into 7 groups, is studied. Such as threats, challenges, risks, resilience of the energy sector, energy supply, and subjects of energy security. Integral assessment methods are used to evaluate the level of energy security compared to threshold values of indicators. Based on these indicators, it was found that the energy sector of Ukraine has been in a state of danger since 2006. Short-term, long-term and strategic measures to improve the level of energy security of Ukraine are presented. The article states that energy security is one of the main components of national security. Today, Ukraine is in the article of energy danger, therefore, to improve the situation, integration with Europe, development of renewable energy, political reforms and electrification of transport are needed.

SoK: Security in Real-Time Systems

Security is an increasing concern for real-time systems (RTS). Over the last decade or so, researchers have demonstrated attacks and defenses aimed at such systems. In this article, we identify , classify and measure the effectiveness of the security research in this domain. We provide a high-level summary [ identification ] and a taxonomy [ classification ] of this existing body of work. Furthermore, we carry out an in-depth analysis [ measurement ] of scheduler-based security techniques — the most common class of real-time security mechanisms. For this purpose, we developed a common metric, “ attacker’s burden ” , used to measure the effectiveness of (existing as well as future) scheduler-based real-time security measures. This metric, built on the concept of “work factor” [ 1 ], is adapted for, and normalized across, various scheduler-based real-time security techniques.

Exploring eustress and fear: A new perspective on protection motivation in information security policy compliance within the financial sector

Information security policy (ISP) compliance has emerged as a significant challenge for contemporary businesses. This underscores the crucial role of employing fear appeals to encourage employee compliance with ISP. However, the existing literature is rife with mixed and inconsistent findings regarding the influence of fear appeals on employees' intentions to comply with ISP. In an effort to address this inconsistency and delve into the mechanisms through which fear appeals impact ISP compliance, we expanded the Protection Motivation Theory by incorporating the Transactional Model of Stress and Coping. Notably, we introduced a positive emotion, eustress, into the mediating mechanism alongside fear. Our hypothesis posited that fear appeals not only generate fear but also evoke eustress, subsequently positively influencing ISP compliance. We conducted a study among financial sector employees and validated our hypothesis using structural equation modeling techniques. Our primary theoretical contributions include highlighting the critical role of eustress in enhancing ISP compliance and proposing a parallel mechanism through which fear appeals impact ISP compliance. From a practical standpoint, we demonstrate that eustress plays a more significant role in motivating employees to comply with ISP. Furthermore, we provide insights into how organizations can design fear appeal messages by incorporating the concept of eustress, thereby optimizing their effectiveness in promoting ISP compliance.

Web3.0 Document Security: Leveraging Blockchain Technology

In the quickly developing landscape of Web3.0, ensuring the security and integrity of digital documents has become paramount. This abstract introduces a groundbreaking solution, "Web3.0 Document Security: Leveraging Blockchain Technology," which harnesses the power of blockchain to revolutionize document transmission and protection. This innovative system leverages the inherent characteristics of blockchain, such as immutability, transparency, and decentralization, to develop a secured and sealed environment for the exchange of digital documents. By utilizing smart contracts, cryptographic hashing, and decentralized storage, this application guarantees the confidentiality, integrity, and authenticity of documents in an interconnected Web3.0 world. This paper provides an overview of the key features, benefits, and implications of adopting a decentralized application as the foundation for secure document transmission in the era of Web3.0 . Keywords— Documents, Data Security, Data Integrity, Secure Transmission, Cryptography, Blockchain, Cloud Technology, Web3

Optimization of the EU and Czech Security System in the Field Of Effectiveness of the Risk Management and Protection System For Security Practices

Risk management and Population protection in the Czech Republic is based on a long tradition and knowledge from dealing with emergencies and crisis situations. However, it appears that there is a need to continuously improve this area of state security policy on the basis of practical experience and deeper analyses. The possibility of optimizing the basic areas of security systems is the subject of security research by a number of Czech authorities and organizations, where the Ministry of the Interior of the Czech Republic and its educational institution, the Police Academy of the Czech Republic in Prague (hereinafter referred to as "PA"), play an important role. At this University focused not only on police topic, but also on the protection of the population and economy, public administration security and civil emergency planning, a number of research activities have been carried out, which are also used in this proposal for the optimization of security systems in the Czech Republic and partly in the EU.

Classic publications in food security research: A bibliometric analysis

AbstractThe article presents classic publications in the field of food security from 1993 to 2022 using bibliometric methods. Data come from the Science Citation Index Expanded (SCI‐EXPANDED) of the Web of Science database. The results show that the years 2010 and 2011 stand out as the most prolific, with a total of three classic articles receiving the highest citations. The articles show that agricultural intensification does not necessarily lead to increased yields but rather contributes to environmental degradation through deforestation, biodiversity loss, and ultimately climate change. The articles highlight the adoption of ecologically friendly methods, natural solution, and technology‐based and sustainable agricultural practices to reduce the impact of climate change and address food insecurity. However, linking agricultural intensification to biodiversity conservation and hunger and the effectiveness of different adaptation models in the increasing variability of extreme events remain complex issues that require further research in the future. Similarly, machine learning research can be used to address food insecurity, especially in crop or plant, and forestry tree breeding, precision agriculture, and so forth.

SoK: Analyzing Privacy and Security of Healthcare Data from the User Perspective

Interactions in healthcare, by necessity, involve sharing sensitive information to achieve high-quality patient outcomes. Therefore, sensitive data must be carefully protected. This article explores existing privacy and security research conducted in the context of healthcare organizations. We conducted a systematic literature review of N =1,553 articles that examine the security and privacy of healthcare data and focus on 80 articles addressing human factors. Key findings show that much of the healthcare security and privacy research is focused on technology (44.11%, 712 articles), with a lack of emphasis on the human element (4.96%, 80 articles). In the subset of user studies, we find that patients and the general public express concerns about privacy and security with technologies like electronic health records (EHRs). Furthermore, our analysis shows that healthcare professionals often have low awareness of risks related to data security. Additionally, our analysis revealed that most research focuses narrowly on large hospitals, neglecting private practices and the unique challenges they face. We conclude by identifying research gaps and providing potential solutions to enable robust data security for sensitive patient data.

GhostEncoder: Stealthy backdoor attacks with dynamic triggers to pre-trained encoders in self-supervised learning

Within the realm of computer vision, self-supervised learning (SSL) pertains to training pre-trained image encoders utilizing a substantial quantity of unlabeled images. Pre-trained image encoders can serve as feature extractors, facilitating the construction of downstream classifiers for various tasks. However, the use of SSL has led to an increase in security research related to various backdoor attacks. Currently, the trigger patterns used in backdoor attacks on SSL are mostly visible or static (sample-agnostic), making backdoors less covert and significantly affecting the attack performance. In this work, we propose GhostEncoder, the first dynamic invisible backdoor attack on SSL. Unlike existing backdoor attacks on SSL, which use visible or static trigger patterns, GhostEncoder utilizes image steganography techniques to encode hidden information into benign images and generate backdoor samples. We then fine-tune the pre-trained image encoder on a manipulation dataset to inject the backdoor, enabling downstream classifiers built upon the backdoored encoder to inherit the backdoor behavior for target downstream tasks. We evaluate GhostEncoder on three downstream tasks and results demonstrate that GhostEncoder provides practical stealthiness on images and deceives the victim model with a high attack success rate without compromising its utility. Furthermore, GhostEncoder withstands state-of-the-art defenses, including STRIP, STRIP-Cl, and SSL-Cleanse.

Qualitative content analysis of actionable advice in information security policies – introducing the keyword loss of specificity metric

Purpose This paper aims to investigate how congruent keywords are used in information security policies (ISPs) to pinpoint and guide clear actionable advice and suggest a metric for measuring the quality of keyword use in ISPs. Design/methodology/approach A qualitative content analysis of 15 ISPs from public agencies in Sweden was conducted with the aid of Orange Data Mining Software. The authors extracted 890 sentences from these ISPs that included one or more of the analyzed keywords. These sentences were analyzed using the new metric – keyword loss of specificity – to assess to what extent the selected keywords were used for pinpointing and guiding actionable advice. Thus, the authors classified the extracted sentences as either actionable advice or other information, depending on the type of information conveyed. Findings The results show a significant keyword loss of specificity in relation to pieces of actionable advice in ISPs provided by Swedish public agencies. About two-thirds of the sentences in which the analyzed keywords were used focused on information other than actionable advice. Such dual use of keywords reduces the possibility of pinpointing and communicating clear, actionable advice. Research limitations/implications The suggested metric provides a means to assess the quality of how keywords are used in ISPs for different purposes. The results show that more research is needed on how keywords are used in ISPs. Practical implications The authors recommended that ISP designers exercise caution when using keywords in ISPs and maintain coherency in their use of keywords. ISP designers can use the suggested metrics to assess the quality of actionable advice in their ISPs. Originality/value The keyword loss of specificity metric adds to the few quantitative metrics available to assess ISP quality. To the best of the authors’ knowledge, applying this metric is a first attempt to measure the quality of actionable advice in ISPs.

Security Patchworking in Lebanon: Infrastructuring Across Failing Infrastructures

In this paper we bring to light the infrastructuring work carried out by people in Lebanon to establish and maintain everyday security in response to multiple simultaneously failing infrastructures. We do so through interviews with 13 participants from 12 digital and human rights organisations and two weeks of ethnographically informed fieldwork in Beirut, Lebanon, in July 2022. Through our analysis we develop the notion of security patchworking that makes visible the infrastructuring work necessitated to secure basic needs such as electricity provision, identity authentication and financial resources. Such practices are rooted in differing mechanisms of protection that often result in new forms of insecurity. We discuss the implications for CSCW and HCI researchers and point to security patchworking as a lens to be used when designing technologies to support infrastructuring, while advocating for collaborative work across CSCW and security research.

Gender Security in EU Member-States and Ukraine

Purpose: This article explores gender security within the economic frameworks of EU member-states and Ukraine. It aims to expand the definition of gender security beyond traditional concepts of conflict resolution and violence prevention, focusing on economic stability and equality. The article analyzes the impact of economic policies, labor market dynamics, healthcare systems, and social policies on gender security, contributing to a broader understanding of the concept. Methods: The study employs a quantitative methodology using a robust set of indicators across demographic, macroeconomic, and social security parameters. These indicators include predefined threshold values for evaluating economic and gender security. Countries are assigned numerical rankings based on their performance against these thresholds, with a focus on female-to-male ratios in key areas. Results and conlcusions: The research reveals significant disparities in gender security across EU member-states and Ukraine. Spain and Sweden demonstrate superior healthcare and socio-economic conditions, reflected in high female life expectancies and low infant mortality rates. In contrast, Bulgaria, Romania, and Ukraine exhibit lower life expectancies and higher infant mortality rates, highlighting challenges in healthcare and economic stability. The study also finds discrepancies in unemployment rates between genders, indicating varied economic conditions and societal norms. Implications: The findings can inform targeted policy interventions in healthcare, employment, and social welfare to address gender-specific challenges. By understanding disparities in gender security, policymakers can design more effective strategies to promote gender equality and economic stability. The article’s comprehensive approach provides a nuanced view of gender security, crucial for shaping policies that address both female and male economic securities. Originality / value: This article contributes to gender security research by providing a comprehensive, gender-centric analysis of economic security in the EU and Ukraine. It stands out from previous studies by integrating a wide range of economic indicators to understand the interplay between economic conditions and gender equality.

Securing internet of things using machine and deep learning methods: a survey

The Internet of Things (IoT) is a vast network of devices with sensors or actuators connected through wired or wireless networks. It has a transformative effect on integrating technology into people’s daily lives. IoT covers essential areas such as smart cities, smart homes, and health-based industries. However, security and privacy challenges arise with the rapid growth of IoT devices and applications. Vulnerabilities such as node spoofing, unauthorized access to data, and cyberattacks such as denial of service (DoS), eavesdropping, and intrusion detection have emerged as significant concerns. Recently, machine learning (ML) and deep learning (DL) methods have significantly progressed and are robust solutions to address these security issues in IoT devices. This paper comprehensively reviews IoT security research focusing on ML/DL approaches. It also categorizes recent studies on security issues based on ML/DL solutions and highlights their opportunities, advantages, and limitations. These insights provide potential directions for future research challenges.

CONSUMER SECURITY AND PRIVACY IN THE METAVERSE: A BIBLIOMETRIC ANALYSIS

The metaverse, one of today's most groundbreaking innovations, offers important opportunities for our future in many areas. However, these opportunities bring with them some critical threats. Security and privacy in the metaverse are among the most current threats that researchers focus on. This study aims to provide a more concrete view of the studies on security and privacy research in the metaverse in the WOS and SCOPUS databases by using bibliometric techniques. In addition, this study assesses the effects of the existing literature and offers a series of suggestions regarding measures that can be taken to protect privacy and security. In the study, 86 studies published in 64 different sources between 2018 and 2024 are analyzed in terms of scientific performance with different bibliometric techniques. According to the prominent findings of the study, it is understood that the highest number of scientific publications were published in 2023; each publication received an average of 17 citations; single-author studies constitute 25% of all studies; and the most publications were published in the Journal of Metaverse. It is thought that the results revealed in this study and the suggested coping strategies regarding privacy and security will contribute to future studies.

AST-DF: A New Webshell Detection Method Based on Abstract Syntax Tree and Deep Forest

Webshell is a kind of web-language-based website backdoor, which is usually used by attackers to control web servers. Due to its dangerous nature, how to detect Webshell effectively has become a hot research topic in current Web security research. With the rapid development of Webshell evasion technology, the existing Webshell detection methods have the problem of insufficient ability to detect unknown Webshells. In order to solve the above problems and achieve effective Webshell detection, this study proposes a Webshell detection method based on the abstract syntax tree (AST) and deep forest (DF) model called AST-DF. AST-DF first extracts the abstract syntax tree from the PHP code; then, the abstract syntax tree sequence is feature extracted and vectorized using N-gram and TF-IDF. Finally, the vectors are imported into the deep forest model for classification to determine whether the PHP code to be detected is a Webshell or not. The experimental results show that AST-DF achieves remarkable effects in the task of detecting PHP-type Webshells, with a 99.61% accuracy rate, and the values of precision, recall, and F1 score are more than 99%.

Customizable Presentation Attack Detection for Improved Resilience of Biometric Applications Using Near-Infrared Skin Detection.

Due to their user-friendliness and reliability, biometric systems have taken a central role in everyday digital identity management for all kinds of private, financial and governmental applications with increasing security requirements. A central security aspect of unsupervised biometric authentication systems is the presentation attack detection (PAD) mechanism, which defines the robustness to fake or altered biometric features. Artifacts like photos, artificial fingers, face masks and fake iris contact lenses are a general security threat for all biometric modalities. The Biometric Evaluation Center of the Institute of Safety and Security Research (ISF) at the University of Applied Sciences Bonn-Rhein-Sieg has specialized in the development of a near-infrared (NIR)-based contact-less detection technology that can distinguish between human skin and most artifact materials. This technology is highly adaptable and has already been successfully integrated into fingerprint scanners, face recognition devices and hand vein scanners. In this work, we introduce a cutting-edge, miniaturized near-infrared presentation attack detection (NIR-PAD) device. It includes an innovative signal processing chain and an integrated distance measurement feature to boost both reliability and resilience. We detail the device's modular configuration and conceptual decisions, highlighting its suitability as a versatile platform for sensor fusion and seamless integration into future biometric systems. This paper elucidates the technological foundations and conceptual framework of the NIR-PAD reference platform, alongside an exploration of its potential applications and prospective enhancements.

Distributed Architectures for Big Data Analytics in Cloud Computing: A Review of Data-Intensive Computing Paradigm

Big Data challenges are prevalent in various fields, including economics, business, public administration, national security, and scientific research. While it offers opportunities for productivity and scientific breakthroughs, it also presents challenges in data capture, storage, analysis, and visualization. This paper provides a comprehensive overview of Big Data applications, opportunities, challenges, and current techniques and technologies to address these issues. This study presents a system for managing big data resources using cloud for the development of data-intensive applications. It addresses even the challenges related to technologies that combine cloud computing with other allied technologies and devices. In addition, the increasing volume, velocity, and variety of data in the era of Big Data necessitate advanced methods for data processing and management. This study delves into the intricacies of data scalability, real-time processing, and the integration of diverse data types. Furthermore, it explores the role of machine learning algorithms and artificial intelligence in extracting meaningful insights from massive datasets.

Adversarially robust and real-time DDoS detection and classification framework using AutoML

ABSTRACT Denial of Service (DoS) attacks target the availability part of the CIA triad (Confidentiality, Availability, and Integrity). A special category of these attacks is the Distributed DoS (DDoS) attack, where the attacker uses a network of compromised systems called a botnet to flood a target server with requests and refuses to serve legitimate users. DDoS attacks can cost an organization millions of dollars in terms of lost revenue, remediation costs, and damage to brand reputation. Hence, all organizations need speedy real-time detection of DDoS attacks. This work presents a DDoS detection and classification framework using the flow-based approach for feature engineering and the AutoML technique. Our detection system is trained on the latest DDoS datasets – CIC-DDoS 2019 and CIC-IDS 2017, which contain various categories of DDoS attacks. We use various tools to perform adversarial attacks on our trained model. We retrain our models using adversarially crafted network packet captures and then test our models for robustness against practical adversarial attacks that an attacker might use to evade detection. Finally, we deploy our model in real-time using a GUI-based tool. Our model achieves a validation accuracy of 99.9% and a low false positive rate of 0.05%.

Hen maternal care inspired optimization framework for attack detection in wireless smart grid network

In the power grid, communication networks play an important role in exchanging smart grid-based information. In contrast to wired communication, wireless communication offers many benefits in terms of easy setup connections and low-cost high-speed links. Conversely, wireless communications are commonly more vulnerable to security threats than wired ones. All power equipment devices and appliances in the smart distribution grid (SDG) are communicated through wireless networks only. Most security research focuses on keeping the SDG network from different types of attacks. The denial-of-service (DoS) attack is consuming more energy in the network leads to a permanent breakdown of memory. This work proposes a new metaheuristic optimization inspired by maternal care of hen to their children called hen maternal care (HMCO) inspired optimization. The HMCO algorithm mimics the care shown by hen for their children in nature. The mother hen is always watchful and protects its chicks against predators. All chickens utilize different calls to designate flying predators like falcons and owls from ground seekers like foxes and coyotes, showing that they can both survey a danger and advise different chickens how to set themselves up. Our method shows greater performance among other standard algorithms.

Blockchain and Cryptocurrency Security: Challenges, Innovations, and Future Directions

Blockchain technology and cryptocurrencies have emerged as transformative innovations with the potential to revolutionize various sectors, including finance, supply chain management, and healthcare. However, their widespread adoption also brings forth significant security challenges. This paper provides an overview of the security issues facing blockchain and cryptocurrency systems, explores current research efforts to address these challenges, and discusses potential future directions in blockchain and cryptocurrency security research. We examine topics such as secure consensus mechanisms, smart contract vulnerabilities, privacy-preserving techniques, and regulatory considerations. By understanding these challenges and exploring innovative solutions, we aim to foster the development of more robust and secure blockchain-based systems.