Security Framework Research Articles

Enhancing Communication Security an In-Vehicle Wireless Sensor Network

Confronting the challenges of securing communication in-vehicle wireless sensor networks demands innovative solutions, particularly as vehicles become more interconnected. This paper proposes a tailored communication security framework for in-vehicle wireless sensor networks, addressing both scientific and technical challenges through effective encryption methods. It segments the local vehicle network into independent subsystems communicating via encrypted and authenticated tunnels, enhancing automotive system safety and integrity. The authors introduce a process for periodic cryptographic key exchanges, ensuring secure communication and confidentiality in key generation without disclosing parameters. Additionally, an authentication technique utilizing the sender’s message authentication code secures communication tunnels, significantly advancing automotive cybersecurity and interconnectivity protection. Through a series of steps, including key generation, sending, and cryptographic key exchange, energy costs were investigated and compared with DTLS and TLS methods. For cryptographic security, testing against brute-force attacks and analysis of potential vulnerabilities in the AES-CBC 128 encryption algorithm, HMAC authentication, and HKDF key derivation function were carried out. Additionally, an evaluation of the memory resource consumption of the DTLS and TLS protocols was compared with the proposed solution. This work is crucial for mitigating risks associated with in-vehicle communication compromises within smart cities.

A blockchain-based deep learning approach for cyber security in next-generation medical cyber-physical systems

<p>Cyber-physical systems (CPSs) have been employed to seamlessly integrate numerous processes and physical components with integrated computing facilities and data storage, aiming to achieve a heightened level of effectiveness and efficiency across various qualitative and quantitative metrics, including technical and organizational aspects. The increased use of the web and the prospering network through IoT (Internet of things) have given a critical open door to CPS to prevail. While this innovation is as of now utilized in programmed pilot flying, advanced mechanics frameworks, clinical checking, modern control frameworks, and so forth, the headway of these frameworks should understand undeniable spotlight on making them proficient and secure. To work on the strength, reliability, and security of these frameworks, specialists can integrate blockchain innovation which has an inbuilt mix of consensual calculations, secure conventions, and circulated information capacity, with the CPS. This introduces an efficient deep learning approach based on blockchain for medical cyber-physical systems (CPS), consisting primarily of two components: a) a blockchain based security framework to protect the medical data and b) the extraction of quintessential features from these data to a classifier for performing the anomaly scans using deep learning. The experimental evaluation demonstrates that the suggested system outperforms existing models, achieving exceptional performance with an accuracy rate of 0.96 and a sensitivity score of 0.95.</p>

A trusted computing framework for cloud data security using role-based access and pattern recognition

A trusted computing framework for cloud data security using role-based access and pattern recognition

Preventing international threats in the context of improving the legal framework for national and regional security

Geopolitical challenges and potential integral threats have put the issue of developing strategies and tools to counter risks and strengthen security on the agenda. The purpose of the study is to conduct a comprehensive assessment of the areas of criminal law development in the context of increasing national and international security. The study uses generalisation, analysis, systematisation, and deduction methods. The study assesses the impact of the Russian invasion of Ukraine, which, using the example of specific illegal actions in Latvia, revealed real risks for the national security of Latvia and the Baltic region. The paper considers crimes related to the violation of bans on participation in armed conflicts, organisation and conduct of military-tactical exercises, and the inadmissibility of assisting a foreign state in an act aimed against another state. Ultimately, such a review reveals the importance and real threat of these criminal offences to state security and identifies approaches for improving legislation in this area. The crime of “espionage” is investigated, which ultimately outlines the structure of such an illegal act and its danger. Considerable attention is given to the examination of torture as a crime against humanity, which is done based on national and international legislation. As a result, the evolution of this criminal offence is considered, various approaches to defining this phenomenon are evaluated, sadism as a particular form of torture is investigated, a classification of similar acts is created, and various types of consequences that could be caused during such a crime are formulated. The practical value of the study lies in the possibility of law enforcement agencies using the findings to form effective strategies for developing modern political and legal tools to prevent international threats

Transforming data‐intensive workflows: A cutting‐edge multi‐layer security and quality aware security framework

SummaryThe data‐intensive workflow application executes tasks on edge servers and cloud platforms in a heterogeneous big‐data computing environment. Cloud and edge servers are vulnerable to node attacks and malicious links due to their wireless connections. Thus, detecting and mitigating rogue nodes in edge server communication environments during workflow execution is crucial. In today's workflow execution landscape, there is a rising emphasis on resolving Quality of Service (QoS) and security concerns within homogeneous execution settings. Workflow execution on diverse computing platforms has received limited research. We are developing an edge‐cloud‐specific Multi‐Layer Security and Quality‐Aware (MLSQA) framework to solve research issues in this area. Node attacks are detected via reputation‐based security features in the MLSQA framework, whereas link attacks are detected using machine learning. A unique trade‐off metrics technique optimizes workflow security and QoS parameters, reducing energy usage and makespan. In addition, a fault‐tolerant, energy‐efficient job offloading technique is described in detail to improve the system's resilience to errors. The experiment is conducted using complex (i.e., memory and CPU intensive) scientific procedures with intermediate job dependencies, namely Inspiral workflow. The MLSQA framework excels in threat detection, demonstrating lower false alarms, reduced energy consumption, and quicker implementation than the recent secure workflow execution architecture.

Management frameworks information technology and cyber risks during the digital transformation of the organization

Problem statement. It is necessary to effectively manage the implementation of digital security measures of an organization to ensure the cybersecurity of an information system in the context of digital transformation and minimize the risk of possible losses and security breaches. Goal. Perform a comprehensive analysis and benchmarking of frameworks in this area. Results. The main documents regulating cybersecurity and IT risks in digital transformation are analyzed, standard guidelines, recommendations and frameworks for information security and cybersecurity management are analyzed. System solutions for the development of a cyber and digital security strategy have been identified. Practical significance. A model of a cybersecurity and business continuity management system in the context of an organization's digital transformation is proposed. Recommendations for the organization's digital security management mechanism are described.

A Security Framework for Addressing Privacy Issues in the Zoom Conference System

The COVID-19 pandemic had a significant impact on many facets of human behavior. Most significantly, it required significant changes to how daily activities were conducted. The majority of individuals were forced to work and communicate from home due to social distancing, which opened the door for more virtual meetings. Since most organizations started allowing their employees to work from home, the majority of online meeting platforms—like Zoom and Microsoft Teams—have become more and more popular. Online meetings were not only used by businesses but also by educational institutes to carry out online learning, hospitals to conduct meetings and certain surgeries, and many other industries. The online meetings are practical and simple to organize, but their information security is not as high as that of conventional meetings. Security and privacy issues resulted from this. The safety of personal information such as names, contacts, and locations, the security of online recordings since sensitive information was discussed in most meetings, the security of data while it was in transit, and the potential for competitors to intercept your business were among the many security issues that were raised. Zoom, as one of the famous online conference systems, faced many global concerns, such as sharing private information with third parties, exposing users to unauthorized bullying calls, and adopting questionable end-to-end encryption processes. Additionally, companies have had virtual meetings hacked, leading to privacy issues since hackers can obtain data illegally. Therefore, this research proposes a security framework to address the privacy issues in the Zoom system by applying a set of governance and technical controls. The governance controls provide a strategic view of how an organization controls its security while implementing the technical controls avoids privacy issues in online conference systems. The proposed framework implements a set of technical controls such as encryption, auditing, authentication, and role-based access control. The results were promising and significantly addressed Zoom's privacy issues.

THE INFLUENCE OF EUROPEAN INTEGRATION ON THE ENTERPRISES OF THE AGRARIAN, FOOD AND PROCESSING INDUSTRY WITHIN THE FRAMEWORK OF FOOD SECURITY OF UKRAINE

Purpose. The purpose of this study is to review of the processes of European integration influence on the enterprises of the agrarian, food and processing industries in the framework of food security of Ukraine in the conditions of war Methodology of research. The theoretical and methodological basis of the article is the fundamental provisions of modern economic science. General scientific and special research methods were used in the study, namely: systematic approach – to summarise the main views of the authors on the above-mentioned issues; monographic approach – for theoretical generalisation of the essence of food security; generalisation – to formulate the author’s own vision of the European integration impact on enterprises; abstract and logical approach – to formulate conclusions. Findings. The place and role of the agro-industrial complex of Ukraine in the national economy are determined. The basic principles and principles of Ukraine’s entry into the European integration process are considered. Attention is focused on the possibilities of the agro-industrial sector of Ukraine after integration into the European Union. It is revealed that European integration is not only economic cooperation and trade, but also a certain worldview. It is substantiated that European integration will provide a real opportunity to create a system of customs regulation that would be effective and in line with the customs procedures and rules of the EU. It will also provide an opportunity to regulate, protect and fiscalize the products of Ukrainian producers when they are exported. The article analyses the impact of European integration on agricultural, food and processing enterprises. Their positive and negative aspects are analysed in the light of the historical realities of today. Originality. The European integration impact on the enterprises of the agrarian, food and processing industries within the framework of Ukraine's food security is highlighted. The prospects for the functioning of the agro-industrial sector of Ukraine in the context of European integration are reviewed. Practical value. The main provisions of the study allow agricultural producers to determine the vectors of European integration processes in Ukraine, taking into account the realities of today. Key words: european integration process, food safety, agricultural, food and processing industry enterprises.

Adaptive Marine Predator Optimization Algorithm (AOMA)-Deep Supervised Learning Classification (DSLC) Based IDS Framework for MANET Security

Adaptive Marine Predator Optimization Algorithm (AOMA)-Deep Supervised Learning Classification (DSLC) Based IDS Framework for MANET Security

A Survey on Secure Framework for Privacy-Preserving Over EHR in Cloud Environment

A Survey on Secure Framework for Privacy-Preserving Over EHR in Cloud Environment

Towards a Hybrid Security Framework for Phishing Awareness Education and Defense

The rise in generative Artificial Intelligence (AI) has led to the development of more sophisticated phishing email attacks, as well as an increase in research on using AI to aid the detection of these advanced attacks. Successful phishing email attacks severely impact businesses, as employees are usually the vulnerable targets. Defense against such attacks, therefore, requires realizing defense along both technological and human vectors. Security hardening research work along the technological vector is few and focuses mainly on the use of machine learning and natural language processing to distinguish between machine- and human-generated text. Common existing approaches to harden security along the human vector consist of third-party organized training programmes, the content of which needs to be updated over time. There is, to date, no reported approach that provides both phishing attack detection and progressive end-user training. In this paper, we present our contribution, which includes the design and development of an integrated approach that employs AI-assisted and generative AI platforms for phishing attack detection and continuous end-user education in a hybrid security framework. This framework supports scenario-customizable and evolving user education in dealing with increasingly advanced phishing email attacks. The technological design and functional details for both platforms are presented and discussed. Performance tests showed that the phishing attack detection sub-system using the Convolutional Neural Network (CNN) deep learning model architecture achieved the best overall results: above 94% accuracy, above 95% precision, and above 94% recall.

A Critical Evaluation of Government Role in Spatial Data Infrastructures for Healthcare Decision-Making

This study critically examines the government's pivotal role in promoting and regulating Spatial Data Infrastructures (SDIs), with a special focus on the healthcare sector. Through an in-depth analysis of global practices, our research highlights the indispensable value of SDIs in enhancing healthcare decision-making, emergency management, and public health monitoring by facilitating the integration and analysis of geospatial and healthcare data. Despite the evident benefits, the integration of diverse geospatial data sources poses significant challenges, including issues of data standardization, privacy, and interoperability across various stakeholders. Our findings underscore the balance governments must achieve in promoting SDI growth while ensuring data security, privacy, and open access. The study draws on a comprehensive review of literature and case studies to explore the effectiveness of current government strategies in advancing SDI capabilities, while also addressing the ethical considerations and potential barriers to innovation that stringent regulation might impose. By advocating for a collaborative ecosystem that supports data standardization alongside flexibility for innovation, this paper aims to provide insights into how governments can optimize the utility of spatially enabled data in healthcare and beyond. Our conclusion calls for strategic government action to promote a balanced approach to SDI governance, highlighting the importance of public-private partnerships, robust data security frameworks, and the fostering of an environment conducive to data sharing and innovation. This research contributes to the discourse on the critical intersection of technology, healthcare decision-making, and governance, offering valuable recommendations for policymakers, healthcare professionals, and stakeholders in the digital and geospatial domains.

Mitigating Vulnerabilities in Large-Scale Network Environments: Strategies and Best Practices

In the modern world of networks, managing multiple threats in large-scale network environments is an essential goal of organizations to protect their system and essential information. Measures to counter threats and sources of vulnerability and the chances of cyber threats and cyber incidences are the primary focuses of this paper. Vulnerability management, network segmentation, and access controls are the first pillars of a sound cybersecurity program. Vulnerability management is a repeated process of discovering, analyzing, documenting, addressing, and checking general and individual protection flaws before an attacker can. Dividing the network becomes effective in preventing threats from affecting certain highly vulnerable areas of the network, as well as improving the levels of security control. Adopting robust identity and access management solutions, including MFA and RBAC, adds another layer of protection to the network by limiting access to networks and resources to only authorized users. Furthermore, the relevance of repeating vulnerability assessments and penetration testing is underlined, as these activities help update the information on the organization’s security and identify insecure positions. However, factors like the increasing pace of vulnerabilities and the necessity to adapt instantly to novel threats show that protection is not easy. Cyber threats are still on the rise, and as a result, the adoption of the spur) advanced technologies like AI in security frameworks boost detection rates and reaction time. The paper concludes by emphasizing the need for constant monitoring and implementing preventive measures to safeguard against emerging and evolving cyber threats.

LPMSAEF: Lightweight process mining-based software architecture evaluation framework for security and performance analysis

The article discusses the need for a lightweight software architecture evaluation framework that can address practitioners' concerns. Specifically, the proposed framework uses process mining and Petri nets to analyze security and performance in software development's early and late stages. Moreover, the framework has been implemented in six case studies, and the results show that it is a feasible and effective solution that can detect security and performance issues in complex and heterogeneous architecture with less time and effort. Furthermore, the article provides a detailed explanation of the framework's features, factors, and evaluation criteria. Additionally, this article discusses the challenges associated with traditional software architecture documentation methods using Unified Modeling Language diagrams and the limitations of code alone for creating comprehensive Software Architecture models. Various methods have been developed to extract implicit Software Architecture from code artifacts, but they tend to produce code-oriented diagrams instead of Software Architecture diagrams. Therefore, to bridge the model-code gap, the article proposes a framework that considers existing Software Architecture in the source code as architectural components and focuses on Software Architecture behaviors for analyzing performance and security. The proposed framework also suggests comparing Software Architecture extracted by different Process Mining algorithms to achieve consensus on architecture descriptions, using visualizations to understand differences and similarities. Finally, the article suggests that analyzing the previous version of a system's Software Architecture can lead to improvements and deviations from planned Software Architecture can be detected using traceability approaches to aid software architects in detecting inconsistencies.

Development of a Mechanism for Assessing Mutual Structural Relations for Import Substitution of High-Tech Transfer in Life Cycle Management of Fundamentally New Products

The emergence of fundamentally new products is conditioned both by the development of techniques and technologies and by the emerging new needs and conditions of economic and social life of society. In this case, the process of achieving product dominance in the market has a progressive cyclical character in the form of a spiral pattern, the movement along which occurs with acceleration. At the current stage of economic development, most states have problems expanding sales of products and capturing new markets. These problems today are described in the works of various scientists; the research of the authors touches upon the issues of economic development. Today’s economic conditions, which are characterized by the application of sanctions pressure on a large scale, do not assess the opposite effect, when the countries that are subjected to this pressure, and the countries that organize this pressure, suffer economic damage. Some suffer damage in the form of reduced ability to produce knowledge-intensive products by providing them with imported materials and components, others who exert this pressure, by reducing the sales of their products, and in this case, it is relevant to build a mechanism for assessing mutual structural links for import substitution of high-tech transfer in the management of the life cycle of radical new products, which in our view is useful for those or other countries. The hypothesis is that the modeling of mutual structural relations of high-tech transfer in the management of the life cycle of radical new products will significantly improve the mechanisms of industrial policy management and national technological security and ensure sustainable economic development. The aim of the study is the task of developing a mechanism of mutual structural links and assessing the synergetic economic effect based on the approach of intersectoral links, interactions and interdependencies. In the course of the research, the following tasks are solved: the necessity to adequately replace high-tech imports within the framework of national technological security is substantiated, for this purpose it is necessary to create unique equipment for the system of RNP production; the assessment of the possibility of realization of such a task is carried out; the assessment of high-tech competencies of science and production, technical and resource readiness (configuration of the RNP system) is necessary, and a complex model for the assessment of structural and mutual linkages in the economy of innovation is developed. The modeling performed by the authors allowed us to assess the structure of domestic high-tech imports and the coefficients of mutual linkage of imports. The practical significance of the study lies in the fact that the conducted research makes it possible to significantly improve the efficiency of management of innovation processes of high-tech transfer to ensure the creation of a system of production of radically new products at the levels of organization, industry or national economy, which will ensure stable economic development. For this purpose, the structure and dynamics of high-tech imports of the Russian Federation have been analyzed and calculated, taking into account critical technologies and industries. The multiplier effects in high-tech industries of the Russian economy taking into account imports were analyzed on the basis of the latest actual detailed data of Rosstat. The estimation of the mutual relationship of imports and application of the developed toolkit for the example “Mechanical equipment, machine tools and other equipment for general or special purposes” is carried out.

Evaluation of security framework for BYOD device in cloud environment

The widespread practice of Bring your own device (BYOD) to work significantly raises cybersecurity risks. All organization’s employees and employers will greatly benefit from this trend. The growth of spyware, malware and other dubious downloads on individual devices has compelled the government to review its data security guidelines. Without user knowledge, hazardous apps are downloaded to personal devices. Both people and governments could suffer tragic consequences as a result of this. In this situation, BYODs are problematic since they can alter policies without permission and release private information. The main goal of the research was to detect fraudulent communications coming from BYOD environments. A network monitoring and managing information configuration settings of mobile devices in the network is established by providing policies and authenticating endpoint devices in the BYOD network using a mix of NAC and MDM. This framework deals with Security Manager (SaaS) as the second module is briefly described. The study's early results were positive and suggested that the framework would lessen access control-related issues.

Threats, Attacks, and Cryptography Frameworks of Cybersecurity in Critical Infrastructures

Critical Infrastructures (CIs), such as healthcare facilities, power grids, transportation systems, and financial institutions, are vital components of a functioning society, with the economy and safety being dependent on them. Nevertheless, they have become increasingly vulnerable to cyber threats and attacks in recent years. The main reason is their inability to quickly adapt to technological changes, employ updated cryptographic frameworks, and implement a thoroughly secure architecture based on their characteristics. In this study, the unique complexities of these systems are highlighted. Various verified cyberattacks that were executed against CIs in recent years are analyzed. Moreover, the general framework of CIs is demonstrated together with the employed technologies and cryptographic primitives. A thorough architecture of said technologies is developed to better understand the targeted components and easily identify potentially hidden threats. Afterwards, threat, adversary, and attack models that target critical systems and services are designed. The purpose is a better comprehension of the systems’ vulnerabilities, attack structures, motives, and targets for assisting CIs’ designers in creating secure frameworks and mechanisms, with the ability to mitigate such threats. Lastly, security controls and cryptography frameworks are demonstrated together with efficient mitigation architectures and implementations from the research community.

A Study of Blockchain and Machine Learning-Enabled IoT Security in Time-Delayed Neural Network Vocal Pattern Recognition to Improve Web-Based Vocal Teaching

With the development of information technology, online vocal teaching is becoming more and more popular, but the sound quality of teaching is also becoming more and more demanding. As online vocal instruction becomes more popular, the need for high-quality sound in these digital environments becomes more critical. This research tackles the problem of improving sound quality in real-time vocal teaching by integrating advanced technologies such as Blockchain and Machine Learning within the Internet of Things (IoT) security framework. We created a vocal recognition model using Time-Delay Neural Network (TDNN) and improved it with Generated Feature Vector (GFV). This integration yields a strong GTDNN vocal recognition system that is specifically designed to secure and optimize web-based vocal teaching. Our experiments show that GTDNN outperforms traditional TDNN and i-vector methods in feature vector extraction, adapting well to different speech environments. In various speech settings, GTDNN's Error Rates (EERs) are impressively low at 11.3%, 12.0%, 4.9%, 6.2%, and 6.1%, indicating superior performance over comparison models. GTDNN has an EER of 9.6% for short-duration speech and 2.3% for long-duration speech. Furthermore, the GTDNN system achieves an overall pass rate of 94% for target speech and an impressive rejection rate for non-target speech, ensuring high accuracy in a variety of speech environments.

A Two-Mode, Adaptive Security Framework for Smart Home Security Applications

With the growth of the Internet of Things (IoT), the number of cyber attacks on the Internet is on the rise. However, the resource-constrained nature of IoT devices and their networks makes many classical security systems ineffective or inapplicable. We introduce TWINKLE, a two-mode, adaptive security framework that allows an IoT network to be in regular mode for most of the time, which incurs a low resource consumption rate, and to switch to vigilant mode only when suspicious behavior is detected, which potentially incurs a higher overhead. Compared to the early version of this work, this article presents a more comprehensive design and architecture of TWINKLE, describes challenges and details in implementing TWINKLE, and reports evaluations of TWINKLE based on real-world IoT testbeds with more metrics. We show the efficacy of TWINKLE in two case studies where we examine two existing intrusion detection and prevention systems and transform both into new, improved systems using TWINKLE. Our evaluations show that TWINKLE is not only effective at securing resource-constrained IoT networks, but can also successfully detect and prevent attacks with a significantly lower overhead and detection latency than existing solutions.

A Systematic Performance Review of Security Methods for the Cyberworld

Governments and organisations globally increasingly recognise the importance of cybersecurity as a critical measure against cyber threats in our highly interconnected society. Establishing robust security measures has become paramount in safeguarding sensitive information and infrastructure. Biometric systems have emerged as critical components in various sectors, including industry, civilian applications, and rhetoric, to enhance security measures. This paper provides an overview of diverse security approaches in the cyber world and examines several research works, highlighting their strengths and weaknesses in implementation. We critically analyse existing methodologies and address potential shortcomings, aiming to improve the effectiveness of security measures. Additionally, we explore emerging trends and novel research directions in the field of biometric and rhetorical security. The study delves into contemporary biometric toolkits, examining their functionalities and applications across domains. Furthermore, we discuss digital ornamental models, evaluating their efficacy in enhancing cybersecurity measures. Through comparative analysis, we identify key differences and areas for improvement in existing security frameworks. In conclusion, this paper proposes a generic computer security model tailored to address the evolving challenges of cybersecurity. We highlight potential applications of this model in society, emphasising the importance of proactive measures to mitigate cyber threats effectively. Through comprehensive analysis and innovative approaches, we aim to contribute to advancing cybersecurity practices in contemporary society.