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Cybersecurity is crucial in today's interconnected world, as digital technologies are increasingly used in various sectors. The risk of cyberattacks targeting financial, military, and political systems has increased due to the wide use of technology. Cybersecurity has become vital in information technology, with data protection being a major priority. Despite government and corporate efforts, cybersecurity remains a significant concern. The application of multi-task learning (MTL) in cybersecurity is a promising solution, allowing security systems to simultaneously address various tasks and adapt in real-time to emerging threats. While researchers have applied MTL techniques for different purposes, a systematic overview of the state-of-the-art on the role of MTL in cybersecurity is lacking. Therefore, we carried out a systematic literature review (SLR) on the use of MTL in cybersecurity applications and explored its potential applications and effectiveness in developing security measures. Five critical applications, such as network intrusion detection and malware detection, were identified, and several tasks used in these applications were observed. Most of the studies used supervised learning algorithms, and there were very limited studies that focused on other types of machine learning. This paper outlines various models utilized in the context of multi-task learning within cybersecurity and presents several challenges in this field. [ABSTRACT FROM AUTHOR]

The article is devoted to a comprehensive and holistic analysis of the legislative framework of Ukraine in the field of cybersecurity. On the basis of a systematic review of normative legal acts, the main trends in the development of legislation have been identified, their compliance with modern challenges and international standards has been analyzed. In addition, it should be noted that attention is paid to the impact of Russian aggression, which continues from the annexation of the Crimea and the occupation of Luhansk and part of Donetsk regions, as well as from the day of the full-scale invasion of the Russian Federation into the territory of our sovereign state to changes in the legislation. The study allows to assess the effectiveness of the current legislation and formulate proposals for its improvement and improvement. The results of the study can be used by state authorities, enterprises and organizations to increase the level of cyber defense and ensure the national security of Ukraine as a whole. The process of formation and formation of the legal framework for ensuring cybersecurity in Ukraine does not stop, but continues to this day, as it is a slow process. Despite the fact that the main and main vectors of state policy in this area have already been defined and outlined, the legislation still needs further improvement to adequately and fully respond to modern cyber threats. The challenges of the digital world require constant updating of the regulatory framework. We believe that in order to further develop and improve legislation in the field of cybersecurity, it is necessary: to improve the regulatory framework taking into account the constant dynamics of cyberspace, also to introduce effective mechanisms of legal protection against cyber threats that constantly exist, especially due to aggression by the Russian Federation, in addition, it is necessary to increase the level of cyber defense of critical infrastructure. Also, in our opinion, it is important and important to ensure the harmonization of national legislation with international standards. We also consider it appropriate to develop innovative, modern legal tools to protect information resources that exist in our country as a whole. That is, a deep analysis of these issues contributes to a comprehensive understanding of cybersecurity problems and allows us to develop effective measures to protect the information space of Ukraine. [ABSTRACT FROM AUTHOR]

The communication revolution has perpetually reshaped the means through which people send and receive information. Social media is an important pillar of this revolution and has brought profound changes to various aspects of our lives. However, the open environment and popularity of these platforms inaugurate windows of opportunities for various cyber threats, thus social networks have become a fertile venue for spammers and other illegitimate users to execute their malicious activities. These activities include phishing hot and trendy topics and posting a wide range of contents in many topics. Hence, it is crucial to continuously introduce new techniques and approaches to detect and stop this category of users. This article proposes a novel and effective approach to detect social spammers. An investigation into several attributes to measure topic-dependent and topic-independent users' behaviours on Twitter is carried out. The experiments of this study are undertaken on various machine learning classifiers. The performance of these classifiers is compared and their effectiveness is measured via a number of robust evaluation measures. Furthermore, the proposed approach is benchmarked against state-of-the-art social spam and anomalous detection techniques. These experiments report the effectiveness and utility of the proposed approach and embedded modules. [ABSTRACT FROM AUTHOR]

This study explored the integration of blockchain and artificial intelligence technologies to enhance the security framework of electronic voting (e-voting) systems. Amid increasing vulnerabilities and cyber threats to electoral integrity, these technologies provided robust solutions by ensuring the immutability of voting records and enabling real-time anomaly detection. Blockchain technology secured votes in a decentralized, tamper-proof ledger, preventing unauthorized modifications, and enhancing transparency. Concurrently, artificial intelligence leveraged predictive analytics to dynamically monitor and respond to potential security threats, thereby ensuring the reliability and integrity of the voting process. This paper presented a dual-technology approach where blockchain's transparency complemented artificial intelligence's (AI) threat detection capabilities, providing a comprehensive security solution for e-voting systems. Through theoretical models and empirical data, we demonstrated significant improvements in transaction throughput, threat detection accuracy, and system scalability. The findings suggested that the strategic application of these technologies could substantially mitigate current e-voting vulnerabilities, offering a pathway to more secure, transparent, and efficient electoral processes globally. [ABSTRACT FROM AUTHOR]

In the face of constantly changing cyber threats, a variety of actions, tools, and regulations must be considered to safeguard information assets and guarantee the confidentiality, reliability, and availability of digital resources. The purpose of this research is to create an artificial intelligence (AI)-driven system to enhance sustainability for cyber threat detection in Internet of Things (IoT) environments. This study proposes a modern technique named Artificial Fish Swarm-driven Weight-normalized Adaboost (AF-WAdaBoost) for optimizing accuracy and sustainability in identifying attacks, thus contributing to heightening security in IoT environments. CICIDS2017, NSL-KDD, and UNSW-NB15 were used in this study. Min-max normalization is employed to pre-process the obtained raw information. The proposed model AF-WAdaBoost dynamically adjusts classifiers, enhancing accuracy and resilience against evolving threats. Python is used for model implementation. The effectiveness of the suggested AF-WAdaBoost model in identifying different kinds of cyber-threats in IoT systems is examined through evaluation metrics like accuracy (98.69%), F-measure (94.86%), and precision (95.72%). The experimental results unequivocally demonstrate that the recommended model performed better than other traditional approaches, showing essential enhancements in accuracy and strength, particularly in a dynamic environment. Integrating AI-driven detection balances offers sustainability in cybersecurity, ensuring the confidentiality, reliability, and availability of information assets, and also helps in optimizing the accuracy of systems. [ABSTRACT FROM AUTHOR]

In today's digital world, cybersecurity has become one of the most pressing issues, as the growing number of connected devices has led to an increase in cyber threats that pose a serious risk to national and international security. In this context, cooperation between countries becomes extremely important to protect sovereignty and ensure cybersecurity at the global level. Ukraine, which has extensive experience in countering cyber threats, is actively involved in international initiatives and cooperation in this area. The article is devoted to the analysis of international cooperation in the field of cybersecurity and Ukraine's role in this process. The study reveals the importance of an effective regulatory framework, in particular the Bug Bounty program and the vulnerability detection mechanism. Ukraine actively cooperates with international partners, which facilitates the exchange of experience and strengthening of cybersecurity. International cooperation with organizations that recognize security as one of their main goals, such as NATO and the European Union, is also of key importance. It is noted that Ukraine, being an active participant in international initiatives and having extensive experience in the field of cybersecurity, continues to play an important role in strengthening global cybersecurity and contributes to the creation of a safer cyberspace for all countries. In the process of ensuring cybersecurity, Ukraine is developing and implementing new programs and strategies, such as Bug Bounty, to help identify and eliminate vulnerabilities in information systems. In addition, an important step is the adoption of the Vulnerability Search and Identification Procedure, which allows business owners and government agencies to request permission to search for weaknesses in their systems. These initiatives allow Ukraine to actively participate in international cybersecurity processes and contribute to improving overall Internet security. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic saw a surge in cyber attacks targeting pharmaceutical companies and research organisations working on vaccines and treatments for the virus. Such attacks raised concerns around the (in)security of bioinformation (e.g. genomic data, epidemiological data, biomedical data, and health data) and the potential cyberbio risks resulting from stealing, compromising, or exploiting it in hostile cyber operations. This article critically investigates threat discourses around bioinformation as presented in the newly emerging field of 'cyberbiosecurity'. As introduced by scholarly literature in life sciences, cyberbiosecurity aims to understand and address cyber risks engendered by the digitisation of biology. Such risks include, for example, embedding malware in DNA, corrupting gene-sequencing, manipulating biomedical materials, stealing epidemiological data, or even developing biological weapons and spreading diseases. This article brings the discussion on cyberbiosecurity into the realms of International Relations and Security Studies by problematising the futuristic threat discourses co-producing this burgeoning field and the pre-emptive security measures it advocates, specifically in relation to bioinformation. It analyses how cyberbiosecurity as a concept and field of policy analysis influences the existing securitised governance of bioinformation, the global competition to control it, and the inequalities associated with its ownership and dissemination. As such, the article presents a critical intervention in current debates around the intersection between biological dangers and cyber threats and in the calls for 'peculiar' policy measures to defend against cyberbio risks in the 'new normal'. [ABSTRACT FROM AUTHOR]

Database systems have consistently been prime targets for cyber-attacks and threats due to the critical nature of the data they store. Despite the increasing reliance on database management systems, this field continues to face numerous cyber-attacks. Database management systems serve as the foundation of any information system or application. Any cyber-attack can result in significant damage to the database system and loss of sensitive data. Consequently, cyber risk classifications and assessments play a crucial role in risk management and establish an essential framework for identifying and responding to cyber threats. Risk assessment aids in understanding the impact of cyber threats and developing appropriate security controls to mitigate risks. The primary objective of this study is to conduct a comprehensive analysis of cyber risks in database management systems, including classifying threats, vulnerabilities, impacts, and countermeasures. This classification helps to identify suitable security controls to mitigate cyber risks for each type of threat. Additionally, this research aims to explore technical countermeasures to protect database systems from cyber threats. This study employs the content analysis method to collect, analyze, and classify data in terms of types of threats, vulnerabilities, and countermeasures. The results indicate that SQL injection attacks and Denial of Service (DoS) attacks were the most prevalent technical threats in database systems, each accounting for 9% of incidents. Vulnerable audit trails, intrusion attempts, and ransomware attacks were classified as the second level of technical threats in database systems, comprising 7% and 5% of incidents, respectively. Furthermore, the findings reveal that insider threats were the most common non-technical threats in database systems, accounting for 5% of incidents. Moreover, the results indicate that weak authentication, unpatched databases, weak audit trails, and multiple usage of an account were the most common technical vulnerabilities in database systems, each accounting for 9% of vulnerabilities. Additionally, software bugs, insecure coding practices, weak security controls, insecure networks, password misuse, weak encryption practices, and weak data masking were classified as the second level of security vulnerabilities in database systems, each accounting for 4% of vulnerabilities. The findings from this work can assist organizations in understanding the types of cyber threats and developing robust strategies against cyber-attacks. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has revolutionized various industries, but the increased dependence on all kinds of IoT devices and the sensitive nature of the data accumulated by them pose a formidable threat to privacy and security. While traditional IDSs have been effective in securing critical infrastructures, the centralized nature of these systems raises serious data privacy concerns as sensitive information is sent to a central server for analysis. This research paper introduces a Federated Learning (FL) approach designed for detecting intrusions in diverse IoT networks to address the issue of data privacy by ensuring that sensitive information is kept in the individual IoT devices during model training. Our framework utilizes the Federated Averaging (FedAvg) algorithm, which aggregates model weights from distributed devices to refine the global model iteratively. The proposed model manages to achieve above 90% accuracies across various metrics, including precision, recall, and F1 score, while maintaining low computational demands. The results show that the proposed system successfully identifies various types of cyberattacks, including Denial-of-Service (DoS), Distributed Denial-of-Service (DDoS), data injection, ransomware, and several others, showcasing its robustness. This research makes a great advancement to the IDSs by providing an efficient and reliable solution that is more scalable and privacy friendly than any of the existing models. [ABSTRACT FROM AUTHOR]

The Internet of Things' (IoT) rapid growth has resulted in a rise in vulnerabilities, making safeguarding IoT systems against intrusions and illegal access a top priority. Intrusion Detection Systems (IDS) are essential for keeping an eye out for irregularities in network traffic. However, the challenge lies in the IDS's ability to detect attacks within high-speed networks while minimizing computational complexity promptly. To improve detection efficiency in IoT networks, we proposed lightweight detection models in this paper that are based on Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), and a GRU-based self-attention mechanism. The Grid Search (GS) algorithm optimizes the models by adjusting the hyperparameters, such as the learning rate and the number of hidden units. The proposed models are evaluated using the ToN-IoT dataset. The achieved detection accuracy for all models is as follows: 97% for GRU, 98.1% for LSTM, 98.4% for Bi-LSTM, and 99% for the GRU-based self-attention mechanism. Furthermore, the GRU-based self-attention mechanism has fewer parameters, which leads to a significant saving in classification time of up to 84% compared to GRU. These findings demonstrate that the GRU-based self-attention mechanism is superior in accuracy and computational efficiency, which makes it particularly effective for real-time intrusion detection in IoT networks. [ABSTRACT FROM AUTHOR]

This article analyses the cybersecurity threats to elections and draws out the lessons learned from the EU (Parliament) elections of June 2024, putting forward recommendations for further improvement. The objective of the analysis is thus manifold. First, it summarises the cyber threats pertinent to elections—those capable of influencing or distorting the election process and outcome. Second, it discusses the measures adopted in preparation for the 2024 EU elections and provides an analysis of how the election campaigns and actual elections across the 27 member states developed. Third, it offers recommendations for improving the cyber resilience of future elections. The analysis will cover both strictly cyber-specific threats and broader hybrid threats. [ABSTRACT FROM AUTHOR]

Cybersecurity has emerged as a critical imperative in contemporary digital landscapes, necessitating heightened awareness and proficiency across all demographic segments. Accordingly, this research has been meticulously crafted to delve into the complexities of cybersecurity awareness, with a specific focus on university students. The study embarks on an exhaustive analysis encompassing the evaluation of cybersecurity awareness levels within the targeted groups, the identification of prevailing issues and practices, and an exploration of novel methodologies, notably gamification, to fortify cybersecurity knowledge and skills among diverse user cohorts. Central to this investigation is the efficacy of gamified learning environments tailored expressly for augmenting cybersecurity awareness among university students. Through a comprehensive examination of existing platforms, methodological frameworks, and user interactions, this research outlines critical trends, challenges, and latent opportunities within the cybersecurity awareness domain, with a specific emphasis on gamification's transformative potential. The study not only identifies key areas for improvement but also proposes innovative solutions rooted in gamified learning paradigms, with the overarching goal of fostering engaging, effective, and sustainable cybersecurity awareness initiatives among students. Drawing upon a synthesis of theoretical constructs, empirical insights, and pragmatic recommendations, this research significantly contributes to the evolving discourse on cybersecurity education. By underscoring the transformative efficacy of gamification as a pivotal tool in cybersecurity awareness initiatives, this study overlays the way for substantial advancements in cybersecurity education paradigms, offering a roadmap for enhancing cybersecurity awareness levels among university students and beyond. [ABSTRACT FROM AUTHOR]

Mobile payment risk has become a critical cybersecurity factor in the cashless society. The outbreak of COVID-19 helped proliferate mobile payments that also bring significant risks to users. Using AI methods, this study analyzed six dimensions of mobile payment risks (financial, privacy, performance, psychology, time, and security) in a survey of 748 respondents from three countries (UK, Taiwan, and Mozambique). The decision tree method was employed to identify and analyze critical perceived risks. The ANOVA test provided insights on the perceived risks between countries. The ANOVA test showed that UK users were concerned about financial and time risks; those in Mozambique were concerned about performance, psychological, and security risks; and those in Taiwan were concerned about privacy risks. The results revealed that decision trees outperformed other methods (such as neural networks, logistic regression, support vector machine (SVM), random forest, and Naïve Bayes models). Performance risk (Taiwan and Mozambique) and security risk (UK) are the most significant factors. Cultural differences influence mobile payment risk perception in different countries. The risk-reduction strategies were also matched to the critical factors by the decision tree. This showed that simplification and risk-sharing strategies were the major tactics in all three countries. The clarification strategy works for Taiwan and Mozambique, which focuses on the benefits of using mobile payments. The results suggest that enterprises should improve and simplify the mobile payment process and collaborate with the third parties to reduce and share cybersecurity risk. [ABSTRACT FROM AUTHOR]

Copyright of IDP: Revista de Internet, Derecho y Politica is the property of Universitat Oberta de Catalunya and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The increasing reliance on computer networks and blockchain technology has led to a growing concern for cybersecurity and privacy. The emergence of zero-day vulnerabilities and unexpected exploits has highlighted the need for innovative solutions to combat these threats. Bug bounty programs have gained popularity as a cost-effective way to crowdsource the task of identifying vulnerabilities, providing a secure and efficient means of enhancing cybersecurity. This paper provides a comprehensive survey of various free and paid bug bounty programs in the computer networks and blockchain industry, evaluating their effectiveness, impact, and credibility. The study explores the structure, incentives, and nature of vulnerabilities uncovered by these programs, as well as their unique value proposition. A comparative analysis is conducted to identify advantages and disadvantages, highlighting the strengths and weaknesses of each program. The paper also examines the role of ethical hackers in bug bounty programs and their contributions to strengthening cybersecurity and privacy. Finally, the study concludes with recommendations for addressing the challenges faced by bug bounty programs and suggests potential future directions to enhance their impact on computer networks and blockchain security. [ABSTRACT FROM AUTHOR]

The growing industry and its complex and large information sets require Big Data (BD) technology and its open-source frameworks (Apache Hadoop) to (1) collect, (2) analyze, and (3) process the information. This information usually ranges in size from gigabytes to petabytes of data. However, processing this data involves web consoles and communication channels which are prone to intrusion from hackers. To resolve this issue, a novel machine learning (ML)-based security-centric approach has been proposed to evade cyber-attacks on the Hadoop ecosystem while considering the complexity of Big Data in Cloud (BDC). An Apache Hadoop-based management interface "Ambari" was implemented to address the variation and distinguish between attacks and activities. The analyzed experimental results show that the proposed scheme effectively (1) blocked the interface communication and retrieved the performance measured data from (2) the Ambari-based virtual machine (VM) and (3) BDC hypervisor. Moreover, the proposed architecture was able to provide a reduction in false alarms as well as cyber-attack detection. [ABSTRACT FROM AUTHOR]

Cyber defense solutions that can adapt to new threats and learn to act independently of human guidance are necessary in light of the proliferation of so-called 'next-generation' cyberattacks. Multi-granularity feature aggregation is a method for detecting network intrusions, but its accuracy is often low due to class imbalance and various classifications of intrusions. To address this issue, this model employs a hybrid sampling algorithm composed of ADASYN and repeated edited nearest neighbors (RENN) for sample processing. The feature-discriminative ability of various assaults is improved by employing channel self-attention at the block level during classification. Finally, an enhanced reptile search algorithm (IRSA) is proposed, which uses a sine cosine algorithm and Levy flight to optimally select the weight of the proposed model. The Levy factor boosts the exploitation capabilities of the search agents, and an algorithm with improved global search capabilities prevents local minimal entrapment by undertaking a full-scale search space. To learn binary and multiclass classification, the model was trained on the CIC-IDS 2017, UNSW-NB15, and WSN-DS datasets. Accuracy and falsehood are just some of the evaluation criteria used in the confusion matrix to determine the system's efficacy. Experimental consequences demonstrate a high detection rate, good accuracy, and a relatively low false alarm rate (FAR), validating the efficacy of the suggested approach. Following that, K4 achieved an accuracy score of 81.99, the precision-recall (PR) was 82.69, the detection rate (D.R.) was 82.12, the F1-score was 80.33, and the FAR was 2.3, all in that order. [ABSTRACT FROM AUTHOR]

There are still alarming gaps among organisations and individuals regarding' cyber-attacks in cyber space and measures to create awareness about cyber insurance. In the realm of cyber space, cyber-attacks can occur, impacting both individuals and businesses. For individuals, this often involves crimes such as identity theft and personal data breaches. For businesses, cyber-attacks can include the theft of intellectual property and other sensitive information. The threat posed by cyber-attacks has grown considerably in recent years. Globally, the survey revealed diverse reasons for not obtaining cyber insurance. It was found that people lacked a clear understanding of cyber insurance products or perceived it as too expensive. [ABSTRACT FROM AUTHOR]

In the virtual world, cybersecurity is essential for safeguarding data integrity and information systems. This paper investigates how cyber threats have evolved over time. From simple hacking efforts to sophisticated organised criminality, cyber threats have an impact on world economies. Numerous cyberattacks, including malware, phishing, and botnet attacks, take advantage of weaknesses and loopholes in digital systems to cause serious security concerns and loss for both persons and businesses. By facilitating better anomaly detection, real-time monitoring, and early threat identification, how the integration of artificial intelligence (AI) capabilities improves cybersecurity can be observed in this review. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Philosophy, Culture & Political Science is the property of Al-Farabi Kazakh National University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Strategic Universe Journal / Univers Strategic is the property of Dimitrie Cantemir Christian University, Institute for Security Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article deals with modern technologies and devices of the Internet of Things, features of providing their cybersecurity, current risks and the latest and effective approaches to high-level cyber defense in the architecture of the Internet of things. The problems of cyber threats in modern internet devices are diverse and generate a significant problem on the path of further promotion of IoT technologies. These are various and complex information risks of cybersecurity for processing and transmission processes in the IoT structures and devices that use different technologies of intervention in the main functionality. As the IoT devices and systems use the Internet and interfaces of the Internet, as well as wireless radio interfaces in combination with mobile operating systems based on high -yielding ARM processors, the risks of IoT cybersecurity are growing significantly and put in question the long - term stable functioning of IoT systems. Sometimes these risks of cybersecurity create a significant problem for information data in IoT systems, and significantly slow down their promotion in a number of industries. Therefore, this problem must be resolved. The work has evaluated and analyzed the problems of cybersecurity in the IoT, the existing approaches are considered and new effective practical provisions and approaches to ensure high cyber defense systems and Internet devices are suggested. The aspects have been considered and the analysis of the problems of cybersecurity of the Internet of things using approaches and methods of the improved protection of data transmission in systems and channels of the Internet of things that are connected to modern Internet information network is carried out. Approaches to more protected data transmission in IoTs, based on a comprehensive combination of known technologies with a combination of their aggregate use and symbiosis with other technologies, are proposed. This enables to enhance cybersecurity and reduce the risks of cyber threats in IoT systems that occur and also takes place in other modern information systems that are often connected and guided via the Internet. These approaches and principles allow to increase the reliability and overall protection of data transmission to IoT, evaluate the main factors of the impact of information threats and to reduce the consequences of their implementation. The prospects for the development of these approaches and methods in complex IoT data exchange systems are considered. The advantages of the method and approaches of cyber defense while the transmission of data to IoT and protected cryptographic processing and transmission of information in the devices and information systems of the Internet of things are described. [ABSTRACT FROM AUTHOR]

Copyright of Forum Pedagogiczne / Pedagogical Forum is the property of Uniwersytet Kardynala Stefana Wyszynskiego w Warszawie and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Romanian Journal of Information Technology & Automatic Control / Revista Română de Informatică și Automatică is the property of National Institute for Research & Development in Informatics - ICI Bucharest and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the urge to secure and protect digital assets, there is a need to emphasize the immediacy of taking measures to ensure robust security due to the enhancement of cyber security. Different advanced methods, like encryption schemes, are vulnerable to putting constraints on attacks. To encode the digital data and utilize the unique properties of DNA, like stability and durability, synthetic DNA sequences are offered as a promising alternative by DNA encoding schemes. This study enlightens the exploration of DNA's potential for encoding in evolving cyber security. Based on the systematic literature review, this paper provides a discussion on the challenges, pros, and directions for future work. We analyzed the current trends and new innovations in methodology, security attacks, the implementation of tools, and different metrics to measure. Various tools, such as Mathematica, MATLAB, NIST test suite, and Coludsim, were employed to evaluate the performance of the proposed method and obtain results. By identifying the strengths and limitations of proposed methods, the study highlights research challenges and offers future scope for investigation. [ABSTRACT FROM AUTHOR]

In our rapidly digitalizing world, the safeguarding of sensitive information stands as a critical concern for organizations across industries. This paper addresses the evolving complexities in data security by advocating the Advanced Encryption Standard (AES) algorithm as a robust defense against the expanding landscape of cyber threats. As financial, healthcare, and educational data transition to digital formats, vulnerabilities in data transmission become more pronounced. To counter these risks and ensure the integrity, confidentiality, and authenticity of essential data, this paper emphasizes the implementation of AES encryption. Thoroughly scrutinizing the widely acknowledged AES algorithm, we highlight its effectiveness and adaptability in securing data. The paper underscores the indispensability of AES in the modern data security milieu, emphasizing its role not only in securing information during transmission and storage but also in decryption, granting authorized users access to protected data. By embracing AES encryption and decryption, organizations can strengthen their defenses against various data-related threats, maintaining the trust and assurance of stakeholders. This work emphasizes the pressing demand for AES encryption and decryption in an era where data security takes precedence, offering valuable insights into their significance and practical application for the protection of crucial digital assets. [ABSTRACT FROM AUTHOR]

This article details Singapore's efforts against disinformation and state-sponsored subversion. The methods examined include both legal and non-legal measures that have been enacted (with the broader canvas, also examined, of what other nations have attempted to shore up their own defences). The article also discusses economic coercion: the issue is not in itself new, but has in recent years been tinged with added potency on account of the wider geopolitical struggle for the commanding heights between the United States and China. Some consideration is also given at the conclusion to the way forward: what nations need to do in the face of emergent challenges (including generative AI) which might further complement the already impressive toolkit of malign actors. [ABSTRACT FROM AUTHOR]

The Metaverse brings together components of parallel processing computing platforms, the digital development of physical systems, cutting-edge machine learning, and virtual identity to uncover a fully digitalized environment with equal properties to the real world. It possesses more rigorous requirements for connection, including safe access and data privacy, which are necessary with the advent of Metaverse technology. Traditional, centralized, and network-centered solutions fail to provide a resilient identity management solution. There are multifaceted security and privacy issues that hinder the secure adoption of this game-changing technology in contemporary cyberspace. Moreover, there is a need to dedicate efforts towards a secure-by-design Metaverse that protects the confidentiality, integrity, and privacy of the personally identifiable information (PII) of users. In this research paper, we propose a logical substitute for established centralized identity management systems in compliance with the complexity of the Metaverse. This research proposes a sustainable Self-Sovereign Identity (SSI), a fully decentralized identity management system to mitigate PII leaks and corresponding cyber threats on all multiverse platforms. The principle of the proposed framework ensures that the users are the only custodians and proprietors of their own identities. In addition, this article provides a comprehensive approach to the implementation of the SSI principles to increase interoperability and trustworthiness in the Metaverse. Finally, the proposed framework is validated using mathematical modeling and proved to be stringent and resilient against modern-day cyber attacks targeting Metaverse platforms. [ABSTRACT FROM AUTHOR]

The increasing threat landscape of Distributed Denial-of-Service (DDoS) attacks makes network security a major concern. These attacks are a serious challenge to the stability and integrity of digital infrastructures. This research paper is an in-depth study on how to enhance network security through the detection and mitigation of DDoS attacks. The study reviews existing literature on DDoS attack mitigation strategies, emphasizing the evolving nature of these threats and the imperative for robust defense mechanisms. The research uses statistical analysis and logistic regression to provide a detailed methodology for distinguishing DDoS attacks from normal network activities. The results show that logistic regression is an effective classification model, providing insights into improved detection measures. Finally, the study concludes by recommending a multi-faceted approach that combines theoretical insights with empirical validation, highlighting the need for stronger network security measures against DDoS attacks and enhancing digital resilience. [ABSTRACT FROM AUTHOR]

The potential breach of access to confidential content hosted in a university's Private Academic Cloud (PAC) underscores the need for developing new protection methods. This paper introduces a Threat Analyzer Software (TAS) and a predictive algorithm rooted in both an operational model and discrete threat recognition procedures (DTRPs). These tools aid in identifying the functional layers that attackers could exploit to embed malware in guest operating systems (OS) and the PAC hypervisor. The solutions proposed herein play a crucial role in ensuring countermeasures against malware introduction into the PAC. Various hypervisor components are viewed as potential threat sources to the PAC's information security (IS). Such threats may manifest through the distribution of malware or the initiation of processes that compromise the PAC's security. The demonstrated counter-threat method, which is founded on the operational model and discrete threat recognition procedures, facilitates the use of mechanisms within the HIPV to quickly identify cyber attacks on the PAC, especially those employing "rootkit" technologies. This prompt identification empowers defenders to take swift and appropriate actions to safeguard the PAC. [ABSTRACT FROM AUTHOR]

This article is devoted to the assessment of the country's sustainable cyberspace strategy by comparing the cyber security strategies of those countries that are economically developed and use advanced information and communication technologies. Based on the components of the National Cyber Security Index, a calculation was made for 10 countries, which made it possible to monitor the effectiveness of their cyber strategies and identify their shortcomings. The calculation is made for the years 2018 and 2021. During this period, no significant critical changes form abnormal indicators, but it is possible to reflect a particular trend. Application of the PROMETHEE method (I and II) and the formation of net flows, determination of importance and comparison of alternatives allowed to carry out an in-depth analysis of various aspects of cyber security, such as protection measures, response to cyber threats and general resistance to cyber-attacks. It is determined that Belgium and Lithuania are the most resistant to threats from cyber-attacks and in the fight against money laundering among the countries considered, as they are rapidly developing and implementing new measures. Their indicators increased by 0.1 values in 2021 compared to 2018. According to the calculations, Great Britain is the most conservative in implementing certain strategies, and its indicators remained at the level of 0-0.2 according to various criteria for the years studied. Most countries improve their indicators by a total of 0.2 values, which reflects their development. Ukraine tends to decrease some indicators by 0.1 value; some are at the same level. The regulatory framework's introduction rate in the cyber security field increases. It shows that Ukraine has many rapidly changing risks, but is trying to achieve flexibility in responding to them. The research findings identify each country's cybersecurity strengths and weaknesses, as well as potential risks and vulnerabilities. Thanks to this analysis, recommendations are made for developing and implementing effective cyber security strategies, both at the national and international levels. The study contributes to the understanding of the complexity of modern cyber threats. It emphasizes the need for continuous improvement and adaptation of protection measures to ensure the stability and security of financial and information systems in a global digital environment. [ABSTRACT FROM AUTHOR]

Due to rising cyber threats, IoT devices' security vulnerabilities are expanding. However, these devices cannot run complicated security algorithms locally due to hardware restrictions. Data must be transferred to cloud nodes for processing, giving attackers an entry point. This research investigates distributed computing on the edge, using AI-enabled IoT devices and container orchestration tools to process data in real time at the network edge. The purpose is to identify and mitigate DDoS assaults while minimizing CPU usage to improve security. It compares typical IoT devices with and without AI-enabled chips, container orchestration, and assesses their performance in running machine learning models with different cluster settings. The proposed architecture aims to empower IoT devices to process data locally, minimizing the reliance on cloud transmission and bolstering security in IoT environments. The results correlate with the update in the architecture. With the addition of AI-enabled IoT device and container orchestration, there is a difference of 60% between the new architecture and traditional architecture where only Raspberry Pi were being used. [ABSTRACT FROM AUTHOR]

The article is devoted to the peculiarities of responsibility for cyber-attacks in the countries of the European Union. It is noted that cyber-attacks on state bodies can be a component of cyber war or hybrid war and at the same time pose a threat to national and global security, as they can lead to the leakage of important information, disrupt the operation of state systems and lead to various negative consequences. So, for example, in Europe, the main goals of hackers from 2022, in addition to Ukraine, became Poland, the Baltic countries, Northern Europe and Germany. The share of cyberattacks on European Union (EU) countries increased from about 10% in the first quarter of 2022 to almost 50% in 2023. In total, 162 cyber-attacks were carried out on Ukraine in 2022; to Poland – 114; Estonia, Lithuania and Latvia – 157; Sweden, Norway, Denmark and Finland – 95, Germany – 57. An increase in the activity of cyber-attacks on the networks of the energy sector, the diplomatic corps, law enforcement agencies, the defense complex, state enterprises and media companies has been established. The main goal of these attacks is to damage the country’s critical infrastructure. Globally, there has been a steady trend towards an increase in the number of targeted cyber-attacks on critical infrastructure facilities in recent years. And therefore, in connection with the possible negative consequences in the case of the implementation of cyber-attacks, it is important to note that ensuring the effective functioning of the country’s cyber defense system remains a high-priority task. Each year underscores the urgency of this challenge as threats in cyberspace continue to grow and become more highly specialized. It was concluded that their public attribution will be important for determining possible responsibility in the countries of the European Union for a cyber-attack, which will include the identification and public announcement of who is behind a specific cyber-attack that affects state, commercial or other objects. [ABSTRACT FROM AUTHOR]

Social networks have become integral to our daily lives, facilitating connections, information sharing, and community engagement. However, concerns regarding privacy and security have emerged with their widespread use. This paper delves into specific privacy risks associated with social media use, including data breaches, identity theft, and cyberstalking. The analysis extends to various security measures, such as encryption protocols, two-factor authentication, and advanced browsing techniques to enhance user protection. In our study, 78% of users reported experiencing specific privacy issues, shedding light on the prevalence and nature of challenges individuals face on social media platforms. These issues encompassed data breaches, identity theft, and cyberstalking, underscoring the urgency of addressing these concerns. Moreover, our research explores strategic approaches for social networks to mitigate these challenges. This involves implementing stringent data protection policies, increasing transparency regarding data usage, and empowering users to exert greater control over their personal information. Beyond academic inquiry, the practical implications of addressing these issues are significant, as they directly impact the security and well-being of social media users. This paper provides a comprehensive overview of the current landscape and emphasizes the importance of proactive measures for safeguarding user privacy and security on social networks. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) is a challenging environment for ransomware threats, and it requires robust detection mechanisms to protect critical infrastructures. This study explores the complex landscape of ransomware attacks in IIoT and suggests proactive detection strategies. To develop an advanced detection model, this research uses the CATBoost algorithm that can handle categorical features by leveraging a comprehensive dataset that captures various attributes of ransomware incidents. The study also enhances the interpretability of the model by incorporating SHAP (SHapley Additive exPlanations) which explains how individual features affect ransomware identification in IIoT environments. Empirical evaluation demonstrates that the model can accurately classify ransomware instances with high precision and recall rates. Moreover, SHAP explanation reveals important features that influence the decisions made by the model thereby improving its interpretability and trustworthiness. The experimental results indicate that customized detection approaches are important and highlight the effectiveness of CATBoost algorithm in strengthening IIoT systems against ransomware attacks. [ABSTRACT FROM AUTHOR]

This research is concerned with the critical domain of cybersecurity in edge computing environments, which aims to strengthen defenses against increasing cyber threats that target interconnected Internet of Things (IoT) devices. The widespread adoption of edge computing introduces vulnerabilities that necessitate a strong framework for detecting cyberattacks. This study utilizes Long Short-Term Memory (LSTM) networks to present a comprehensive approach based on stacked LSTM layers for detecting and mitigating cyber threats in the dynamic landscape of edge networks. Using the NSL-KDD dataset and rigorous experimentation, this model demonstrates its ability to detect subtle anomalies in network traffic, which can be used to accurately classify malicious activities while minimizing false alarms. The findings highlight the potential of LSTM-based approaches to enhance security at the edge, providing promising avenues for strengthening IoT ecosystems' integrity and resilience against emerging cyber threats. [ABSTRACT FROM AUTHOR]

This article describes a massive cryptographic scheme that can safeguard IoT communication paths. A combination of algorithms makes the technique operate. Communication security is handled differently by each algorithm. Elliptic Curve Cryptography (ECC), SHA-256 Secure Data Hashing, HMAC Message Authentication, and Merkle Tree Structures Decryption and Verification are used. Ablation is used to determine how each strategy increases security. The paper emphasizes that the algorithms function effectively together, demonstrating their importance for cyberdefense and surveillance. The recommended strategy is evaluated and found to operate better across key parameters. [ABSTRACT FROM AUTHOR]

The ubiquity of interconnected devices within the Internet of Things (IoT) paradigm has revolutionized modern connectivity, simultaneously amplifying the susceptibility of networks to diverse security threats. This study addresses the pressing necessity for robust intrusion detection mechanisms tailored for IoT networks. Utilizing a simulated dataset reflecting a spectrum of network intrusions within a military environment, the research employs sophisticated methodologies, notably harnessing Decision Tree (DT) algorithms optimized via Grey Wolf Optimization (GWO) for hyperparameter tuning. The investigation meticulously evaluates and refines intrusion detection mechanisms, emphasizing the pivotal role of feature importance analysis in fortifying network security. Results demonstrate the efficacy of the optimized DT algorithm in the precise classification of network traffic, illuminating key attributes instrumental for intrusion detection. These findings underscore the significance of adaptive and interpretable detection strategies in mitigating evolving threats within IoT networks, advocating for resilient approaches to bolster network security. [ABSTRACT FROM AUTHOR]

The modern world is becoming increasingly dependent on the security of critical infrastructure facilities (CIF), which is monitored by UAVs, their fleets and multifunctional fleet systems (MFS UAVs). The UAV MFS have a complex digital infrastructure (DIS). The DIS is based on new information technologies that have certain security deficiencies and create new cyber threats, in particular, due to specific vulnerabilities that can be exploited externally. The provision of cyber security of the CIS of the MBF of UAVs has been studied thanks to the development of a sequence of analysis of cyber threats using the IMECA procedure. An overview of existing cyber security assessment methods and their limitations was conducted; developed models of the OKI monitoring system based on the UAV MBF; analyzed cyber threats to its CIS; the criticality of cyber attacks and the impact of countermeasures; formulated recommendations for ensuring cyber security and general conclusions based on research results. A method of ensuring cyber security of the CIS of the MBF UAV was created, which consists of determining its specific features as an object of cyber threats, analyzing violators, vulnerabilities, risks of critical violations and choosing countermeasures, the use of which allows you to increase the level of cyber security and reliability of the monitoring system and ensure a temporary response to cyber threats. [ABSTRACT FROM AUTHOR]

Due to the increasing cybersecurity threats, especially to a state's critical infrastructure, there is a growing need for the development and implementation of effective methods for assessing the level of cybersecurity. The critical infrastructure of a state (such as energy, transportation, medical facilities, etc.) is becoming increasingly dependent on information technologies. Security breaches in these technologies can have serious consequences for citizens' lives and health, as well as economic losses. Many countries are imposing various regulatory requirements regarding cybersecurity, such as GDPR in the European Union or NIST in the United States. This compels organizations managing critical infrastructure to actively seek and implement effective methods for assessing cybersecurity levels. The rapid technological progress leads to the emergence of new attack methods, necessitating the allocation of various resources (financial, human, time, etc.) to develop new, more effective means of enhancing cybersecurity. Assessing the level of cybersecurity enhancement will subsequently allow for evaluating the effectiveness of resources invested in security. The developed method for assessing the enhancement of cybersecurity levels of critical infrastructure objects of the state is based on a model of a characteristic system aimed at assessing the state of cybersecurity in Ukraine. It involves procedures for forming linguistic variables, interval fuzzification, building benchmarks, forming sets of inspection object characteristics, the primary measurement process, forming basic pairs and associative rules, and visualization of results. These are formalized into respective stages, enabling the implementation of the process of assessing the cybersecurity state of critical infrastructure inspection objects. The described method can become an effective tool for analyzing and evaluating the level of cybersecurity of the state's critical infrastructure with the aim of ensuring national security and protection against cyber threats. Further development should focus on building a system that allows for automating the process of assessing the enhancement of cybersecurity levels at inspection objects. [ABSTRACT FROM AUTHOR]

Background: The topic of cyber security of organizations is becoming more relevant for modern societies. In that area, both scientific research and practical applications of various platforms, technologies, and tools for guaranteeing security levels have undergone serious development in recent years. The research objective is based on a study of leading literature sources and the accumulated experience in the subject area to propose and approve an adequate model for ensuring cyber security of an educational institution. Methods: Different organizations adopt different approaches and apply different methods to create systemic conditions for ensuring cyber security. The methods used in the research are based on the specifics of the business processes that take place in the organizations. Based on them, the specifics of educational organizations are highlighted, which determine the creation of a relevant concept of cyber security with appropriate technological measures. Results: The concept proposed in the present study is based both on the current legal framework of the European Union and the Republic of Bulgaria, as well as on good practices and approaches in the subject area. The concept has been implemented and the presented results prove its usefulness for the educational organization. The period to which the empirical part of the study refers is 2022-2023. Conclusions: The main results of the study are in the direction of achieved monitoring of the external perimeter of the organization, implemented monitoring of user behaviour, risk management of information assets, and increased cyber security of the organization. [ABSTRACT FROM AUTHOR]

The contemporary cyber threat landscape presents a complex tapestry of challenges, featuring diverse actors ranging from non-state entities like organized crime syndicates and terrorist organizations to state-sponsored operatives affiliated with nations such as China, Russia, and Iran. Motivations driving these actors span from financial gain to ideological pursuits and geopolitical objectives, resulting in a wide range of cyber operations aimed at individuals, businesses, and governments worldwide. These highprofile incidents underscore the disruptive potential, and strategic implications need of a comprehensive Shipping Industry's Cyber Counterintelligence approach, to safeguard critical assets and operations in an increasingly digitized environment. [ABSTRACT FROM AUTHOR]

This article provides an in-depth analysis of the Cybersecurity Framework version 2.0, introduced by the National Institute of Standards and Technology (NIST) in early 2024. CSF 2.0 is designed to facilitate effective management and mitigation of cybersecurity risks for diverse organizations, regardless of their size and industry. The article explores key components of the framework, such as the Core (CSF Core), Organizational Profiles (CSF Organizational Profiles), Security Tiers (CSF Tiers), and provides insights into their utilization for enhancing organizational cybersecurity practices. Emphasis is placed on the flexibility of the framework, allowing organizations to adapt their approaches to cybersecurity management according to their unique risks and needs. CSF 2.0 is considered a crucial tool aimed at promoting cybersecurity improvement across all organizational levels, irrespective of their technical maturity. The article also offers a comprehensive overview of the potential uses of CSF resources for enhancing cybersecurity practices and underscores the importance of continuously refining cybersecurity management strategies to effectively counter growing cyber threats. [ABSTRACT FROM AUTHOR]

In the modern digital world, where a significant portion of our daily activities takes place online, cybersecurity becomes an integral component of national security for every state. Particularly crucial is the protection of critical infrastructure, which ensures the functioning of the economy, communications, and society as a whole. Consequently, the development and implementation of a system for assessing the improvement of cybersecurity posture for critical infrastructure inspection objects become an extremely pressing task for governments and organizations. The proposed structural model of the system for assessing the enhancement of cybersecurity posture for critical infrastructure inspection objects of a state, which utilizes a database of cybersecurity measures, rules and standards, as well as modules for generating current values, phase formation, and formation of LV, formation of inspection characteristics, and proximity parameters, will enable the evaluation of the improvement in the level of cybersecurity posture of critical infrastructure objects in a loosely formalized fuzzy environment. In this context, its implementation becomes a critically important step in ensuring the security of the state's critical infrastructure in the face of constantly increasing cyber threats. It contributes to enhancing readiness and resilience to potential cyber-attacks, which is an indispensable component of national security strategy. [ABSTRACT FROM AUTHOR]