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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]

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]

Sensors and wearable technology are used to accumulate health-related data from the visually impaired (VI) and other special persons. This health-related data is disseminated via an open network channel, which poses numerous threats and requires rigorous attention for its protection. An appropriate security framework must be implemented on both the sender and the recipient sides to ensure the privacy and security of sensitive medical information for VI and disabled persons. The telehealth system will then benefit the VI and disabled people by improving their quality of life, telemonitoring, saving time and money, providing online support, and increasing the degree of trust and reliance on the system and the treatment of doctors. These practical benefits are not just theoretical but potentially life-changing for the VI and disabled community. Strong and flawless mutual authentication is necessary for the telehealth system to secure and preserve the privacy of VI and disabled people. Therefore, this article proposes a privacy-preserving asymmetric-based, lightweight, and robust security framework for the telehealth system operationalized for VI and disabled people. The proposed security framework alleviates all threats to sensitive information and preserves the privacy and security of the whole telehealth system. The security of the proposed framework has been verified through a well-known Real-Or-Random (RoR) model and simulated through a ProVerif software verification toolkit, while the performance analysis has been tackled by measuring computation, storage, and communication costs. The comparative analysis result revealed that the proposed framework outperforms its competitors, further reinforcing the hope about its potential impact. [Display omitted] • To design a fast and secure protocol for securely authenticating visually impaired (VI) and disabled people of the telehealth system in the digital world. • To present a secure mechanism that not only protects the privacy of VI and disabled people when transmitting personal sensitive information remotely but also opens up new possibilities for their participation in telehealth. • To confirm the security of the proposed authentication protocol formally through the Real-Or-Random (RoR) model and ProVerif and informally through discussions of known attacks. • To conduct a comprehensive performance analysis of the proposed protocol, considering storage, communication and computation costs in providing a robust and reliable solution. • To comparatively analyze the proposed security mechanism with state-of-the-art mechanisms by scrutinizing its efficacy and robustness based on a set of predefined criteria. This will provide a clear understanding of how our protocol stands against existing solutions. [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]

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 widespread adoption of emerging information and communication networks has increased organizations' vulnerability to cyberattacks. These attacks – including data breaches, ransomware, and other cyber threats – pose significant economic risks to the stability of modern businesses. This study offers a comprehensive analysis of the economic impacts of cyberattacks within the context of these emerging networks and outlines effective cyber risk management strategies to enhance organizational resilience. The research elucidates the complex economic repercussions of cyber threats by conducting a rigorous literature review and an in-depth case study of the 2019 ransomware attack on Norsk Hydro ASA. The findings underscore the critical necessity for robust cybersecurity measures in the evolving digital landscape. [ABSTRACT FROM AUTHOR]

Businesses increasingly rely on digital technologies in the digital age, making them vulnerable to cyberattacks. These attacks, which range from data leaks to ransomware, can cause significant economic damage. Effective cyber risk management strategies that include preventive, reactive, and continuous measures can significantly reduce the risk and impact of such attacks. Norsk Hydro's case study illustrates the importance of a robust incident response plan and continuous improvements in cybersecurity measures. This study examines the economic impact of cyberattacks on organizations and identifies effective cyber risk management strategies that can strengthen organizational resilience. The methodology is based on a light literature review, including scientific and grey literature. It also includes an analysis of a real-world case study: the ransomware attack on Norsk Hydro ASA in 2019. The results show that cyberattacks cause direct costs. Furthermore, this study contributes to the academic discourse on cybersecurity and economic resilience by providing evidence-based insights and practical recommendations for businesses. Companies that invest in comprehensive cyber risk management strategies can ensure economic stability and gain competitive advantage by better managing the complexities of the digital landscape. [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.)

Introduction: Traditional network intrusion detection systems (NIDS) face significant challenges in detecting ever-evolving cyber-threats. With the evolution of cyber-attacks, comes a mounting requirement for predictive methods which are competent in identifying both familiar and unknown threats with an effective level of accuracy. Objective: This study is motivated to apply Generative Adversarial Networks (GAN) technology in NIDS for synthetic data generation, so when the NIDS use this technique generates high-quality fake samples which will tremendously increase its accuracy and decrease false positives. In this paper, we aim to evaluate the performance of GAN-boosted NIDS in different environments, such as novel obfuscated and adversarial attacks. Methods: In this study, was developed and trained a GAN by large datasets like UNSW-NB15 and CICIDS2017 using the proposed methodology. The performance of the GAN model was compared with classical machine learning models — Support Vector Machines (SVM) and Random Forests (RF)— via various evaluation metrics such as detection accuracy, false positive rate, and robustness to attacks. Furthermore, expert interviews were included for the qualitative aspects of how practitioners felt about deploying GAN-enhanced NIDS in reality. Results: Along with enhancing detection capabilities, the study will also explore the computational and operational effects of incorporating GAN into existing cybersecurity systems. Findings indicate that the GAN-based system enhances detection accuracy to 95.8% and reduces false positive rate to 2.4%. We additionally discuss the execution of these systems, the necessary deployment process, computing and real-time performance trade-offs, and offer guidance for maximizing resource utilization. The system displayed a performance better than of detecting novel and obfuscated attacks with an accuracy of 88.2%. It also showed resistance to adversarial attacks, keeping detection rates above 90% for various attack vectors. Conclusions: The results indicate that GANs are promising to improve NIDS by increasing its detection and robustness accurately. Nonetheless, improved research and development demands to ensure that GANs meet practical requirements are required due to high computational demands and integration challenges associated with implementation.

Sensors and wearable technology are used to accumulate health-related data from the visually impaired (VI) and other special persons. This health-related data is disseminated via an open network channel, which poses numerous threats and requires rigorous attention for its protection. An appropriate security framework must be implemented on both the sender and the recipient sides to ensure the privacy and security of sensitive medical information for VI and disabled persons. The telehealth system will then benefit the VI and disabled people by improving their quality of life, telemonitoring, saving time and money, providing online support, and increasing the degree of trust and reliance on the system and the treatment of doctors. These practical benefits are not just theoretical but potentially life-changing for the VI and disabled community. Strong and flawless mutual authentication is necessary for the telehealth system to secure and preserve the privacy of VI and disabled people. Therefore, this article proposes a privacy-preserving asymmetric-based, lightweight, and robust security framework for the telehealth system operationalized for VI and disabled people. The proposed security framework alleviates all threats to sensitive information and preserves the privacy and security of the whole telehealth system. The security of the proposed framework has been verified through a well-known Real-Or-Random (RoR) model and simulated through a ProVerif software verification toolkit, while the performance analysis has been tackled by measuring computation, storage, and communication costs. The comparative analysis result revealed that the proposed framework outperforms its competitors, further reinforcing the hope about its potential impact.

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.

As artificial intelligence (AI) is becoming increasingly integrated into critical infrastructures, it brings about both transformative benefits and unprecedented risks. AI has the potential to revolutionize the efficiency, reliability, and responsiveness of essential services, but it can also offer these benefits along with the vulnerability to a growing array of sophisticated adversarial attacks. This paper explores the evolving landscape of adversarial threats to AI systems, highlighting the potential of nation-state actors to exploit these vulnerabilities for geopolitical gains. A range of adversarial techniques is examined, including dataset poisoning, model stealing, and privacy inference attacks, and their potential impact on sectors such as energy, transportation, healthcare, and water management is assessed. The consequences of successful attacks are substantial, encompassing economic disruption, public safety risks, national security implications, and the erosion of public trust. Given the escalating sophistication of these threats, this paper proposes a comprehensive security framework that includes robust incident response protocols, specialized training, the development of a collaborative ecosystem, and the continuous evaluation of AI systems. The findings of this study 11 underscore the critical need for a proactive approach to AI security in order to safeguard the future of critical infrastructures in an increasingly AI-driven world.

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.

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]

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]

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.)