Cybersecurity Problems Research Articles

Networked Control of a Small Drone Resilient to Cyber Attacks

With increasing advances in networked systems and networked control systems in everyday life, the problem of cybersecurity becomes crucial. Moreover, in some applications like small UAVs, the safety and integrity of the system and its surroundings are highly susceptible to cyberattacks. In this context, the current paper proposes a resilient networked control approach. The control structure is split into an inner and an outer loop. The outer position control loop uses measurements from motion cameras connected to a remote computer, while the commands are sent through the network. We consider the resilience problem for two types of cyberattacks: denial of service (DoS), emulated as an increase in the network transmission delay, and man in the middle (MitM), emulated as additive input disturbances. The mitigation for the DoS attack is performed through the help of a reference governor (RG), which uses the delay estimates and the system’s model to predict future safety violations and adapts the reference accordingly. The MitM attack is mitigated by an unknown input disturbance observer (UIDO) together with a RG. Experimental results on a Parrot Mambo drone show that both types of attacks are rejected successfully, ensuring a safe and stable flight.

An Evaluation of the Security of Bare Machine Computing (BMC) Systems against Cybersecurity Attacks

The Internet has become the primary vehicle for doing almost everything online, and smartphones are needed for almost everyone to live their daily lives. As a result, cybersecurity is a top priority in today’s world. As Internet usage has grown exponentially with billions of users and the proliferation of Internet of Things (IoT) devices, cybersecurity has become a cat-and-mouse game between attackers and defenders. Cyberattacks on systems are commonplace, and defense mechanisms are continually updated to prevent them. Based on a literature review of cybersecurity vulnerabilities, attacks, and preventive measures, we find that cybersecurity problems are rooted in computer system architectures, operating systems, network protocols, design options, heterogeneity, complexity, evolution, open systems, open-source software vulnerabilities, user convenience, ease of Internet access, global users, advertisements, business needs, and the global market. We investigate common cybersecurity vulnerabilities and find that the bare machine computing (BMC) paradigm is a possible solution to address and eliminate their root causes at many levels. We study 22 common cyberattacks, identify their root causes, and investigate preventive mechanisms currently used to address them. We compare conventional and bare machine characteristics and evaluate the BMC paradigm and its applications with respect to these attacks. Our study finds that BMC applications are resilient to most cyberattacks, except for a few physical attacks. We also find that BMC applications have inherent security at all computer and information system levels. Further research is needed to validate the security strengths of BMC systems and applications.

Advanced Threat Recognition: Harnessing Machine Learning for Intrusion Detection

Conventional intrusion detection systems (IDS) are frequently inadequate to combat complex cyber threats in the changing cybersecurity landscape. To create a reliable intrusion detection system, this work investigates the use of machine learning, more especially the RandomForestClassifier method. The study highlights that in order to maximize model training, careful data preprocessing—including feature selection, label encoding, and scaling—is essential. It also uses Optuna for hyperparameter adjustment to improve the classifier's performance. As a scalable and dependable response to contemporary cybersecurity issues, the findings show how effective machine learning is in identifying sophisticated attacks. The outcomes show a high detection accuracy and resilience to complex intrusions, confirming machine learning's potential as a scalable and reliable answer to today's cybersecurity problems

Exploring Machine Learning Algorithms for Cyber Attacks’ Classification

Cyber security is like a race between defensive and offensive capabilities. Day by day the attacks are increasing consequently the preventive mechanisms are also raising. The technological evolution must address the cyber security problem effectively. Cyber security is always the cutting-edge technology to which Machine learning added potentiality. Machine learning plays a crucial role cyber-attacks in various dimensions. The recent advancements in usage of Machine learning towards cyber security preventive and detective measures clearly indicates that the Cyber security certainly accelerates. But at the same time the threat framework also takes the advent of innovative machine learning models. In this paper we are intended to explore various machine learning algorithms to classify cyber-attacks, which are very useful in designing efficient threat defensive models

Internet of Things-enhanced blockchain technology: an overview of advancements and prospects

Through a systematic review of the literature, this study provides a comprehensive summary of the latest findings on the acceptance and application of blockchain technology in supply chain management. It was founded on a comprehensive analysis of thirty studies published in prestigious journals. The need for secure, decentralized AI systems to thwart potential cyberattacks is growing as AI is increasingly used in cybersecurity. The ideal choice for enhancing the security and privacy of AI systems is blockchain technology (BT), due to its immutable and decentralized data storage properties. This comprehensive literature review focuses on the integration of decentralized IT with cybersecurity. It provides a comprehensive BT and IoT cybersecurity taxonomy to get you started. This work contributes to a more sophisticated understanding of the integration between BT and IoT by demonstrating compelling results that highlight the benefits of these connections for cybersecurity. These findings are coupled with the potential cybersecurity advantages and difficulties of IoT. It also addresses the beneficial uses of blockchain-enabled IoT to solve cybersecurity problems. Finally, this paper addresses future directions for blockchain-based cybersecurity research across a range of sectors, including industry, agriculture, IoT, and health.

Research on the Obstacles and Countermeasures to the Protection of Children's Rights and Interests in the Network Environment

This survey is based on the background of the era of rapid development of the Internet and the tendency of the younger age of Internet users, as well as the problem of children encountering more and more risks in the Internet environment. At present, the safety of children's network use in China has received more and more attention, but the specific aspects of prevention of network safety problems still need to be improved. This paper analyses the common problems that children in the online environment are mainly exposed to the risks of cyberbullying, identity information leakage, and the influence of violent and bloody information. The analysis of this paper concludes that children's cybersecurity problems are characterized by hiddenness, children's lack of self-awareness, and insufficient social attention. Based on this, the paper makes the following recommendations, the protection of children's Internet use should be carried out in a combination of online and offline ways, and the government, companies, schools, families, and individuals should make a joint effort.

Artificial intelligence applications in solar energy

Renewable energy research has become significant in the modern period owing to escalating prices of fossil fuels and the pressing need to reduce greenhouse gas emissions. Solar energy stands out among these sources due to its abundance and global accessibility. However, its weather-dependent and cyclical nature add inherent risks, making effective planning and management difficult. Soft computing technologies provide attractive solutions for modeling such systems, while machine learning and optimization techniques are gaining popularity in the solar energy industry. The current literature highlights the growing use of soft computing technologies, emphasizing their potential to address difficult challenges in solar energy systems. To effectively reap the benefits, these strategies must be seamlessly connected with emerging technologies like the Internet of Things (IoT), big data analytics, and cloud computing. This integration provides a unique opportunity to improve the scalability, flexibility, and efficiency of solar energy systems. Researchers can use these synergies to create intelligent, linked solar energy ecosystems capable of real-time optimization of energy production, delivery, and consumption. These technologies have the potential to transform the renewable energy environment, allowing for more resilient and sustainable energy infrastructures. Furthermore, as these technologies improve, there is a growing demand for trained experts to address associated cybersecurity problems, assuring the integrity and security of these sophisticated systems. Researchers may pave the road for a more sustainable and energy-efficient future by working collaboratively and using interdisciplinary methodologies.

Better Prevention Than Cure: Cybersecurity Risk and Clawback Provision

ABSTRACTResearch Question/IssueThe escalation of online transactions recently has driven the digitalization trend, which features both emerging opportunities and associated risks, across businesses. This study examines the impact of cybersecurity risk on the adoption of clawback policies among US listed firms during the 2008–2018 period.Research Findings/InsightUsing fixed‐effect estimators, I reveal that clawback adoptions are more likely when cybersecurity risk is on the rise. This effect is transmitted via three channels of multiple business objectives, manager's quiet life, and corporate culture. Furthermore, this effect is diminished among firms with a greater proportion of nonindependent co‐opted directors on board.Theoretical/Academic ImplicationsAlthough clawback policies are often linked to corporate misreporting, this study broadens the conventional research direction by addressing the decision‐making of clawback policy adoption from the data security perspective. The finding of this study implies an extension of clawback endorsement beyond the corporate reporting purpose in the digitalized era.Practical/Policy ImplicationsThis study's findings suggest that firms value the preventive property of clawback policies on corporate misconduct and that firms factor cybersecurity risk into clawback adoption decision‐making. Given that clawback policies extensively tie CEOs' wealth with corporate integrity, early adoptions of this recoupment scheme can be essential to resolving the contemporary cybersecurity problems of the adopted firms.

Using ChatGPT in construction projects: unveiling its cybersecurity risks through a bibliometric analysis

ChatGPT, a large language model chatbot by OpenAI, has increasingly become a part of employees’ day-to-day activities in numerous industries, including construction, and researchers have looked into this tool since its first release in late 2022 to assist in different fields. One of the benefits of such tools can be related to improved efficiency; however, it raises data privacy and security concerns. Considering the increasing reliance on information technology and operational technology for enhanced productivity, accuracy and quality in projects, these concerns also affect the construction sector. This study presents an overview of the existing literature on the applications of ChatGPT in the construction sector, highlighting its potential to revolutionize various resource-intensive tasks in projects and the related cybersecurity risks. VOSviewer is used for bibliometric analysis of academic publications and to identify the relevant cybersecurity problems. The identified issues are categorized into three main groups and discussed in the context of construction applications. Suggestions are provided to address identified concerns. This paper highlights the importance of ensuring the secure deployment of ChatGPT in the construction sector, a subject that has not been explored in the existing literature.

Cybersecurity governance and digital finance: Evidence from sovereign states

Digital finance provides unprecedented development opportunities for countries, especially in times of epidemic, and digital transformation has become an important means of accelerating economic development and maintaining social stability. However, digital finance relies on Internet infrastructure, and cybersecurity challenges have increased, bringing severe security risks to digital finance. Therefore, safeguarding network security has become a key cornerstone for the development of digital finance. This paper analyzes and explores the main cybersecurity problems faced in the era of big data from the perspective of the current development status of digital finance, and at the same time shows some new initiatives currently underway in the international arena in order to meet the challenges posed by cybersecurity and to help navigate the sustainable development of global digital finance.

ДОСЛІДЖЕННЯ КОНТЕКСТНО-ЧУТЛИВОГО АЛГОРИТМУ МОНІТОРИНГУ КІБЕРБЕЗПЕКИ НА ОСНОВІ РЕКУРЕНТНИХ НЕЙРОННИХ МЕРЕЖ

The most common problems faced by modern information and communication systems (ICS) in the context of combating cyber threats were examined in the paper. The importance of ensuring the reliable operation of ICS, and protecting their users' private data from unauthorized interception or destruction was emphasized. The main principles of effective protection of ICS systems against possible interference in their work were defined. The classification of cyber threats and their impact on the functioning of information systems was presented. Features of the use of modern information technologies were determined, such as machine learning (ML), and recurrent neural networks (RNN) for increasing the effectiveness of detecting and preventing such threats, speeding up the process of calculating large volumes of information about various aspects of the work of information and communication systems. The parameters of the analysis of ICS behavior, which indicate the presence of problems in cyber security, were studied. The features and advantages of deploying RNN in ICS were analyzed, which makes it possible to simplify the tasks of cyber defense. A modified context-sensitive algorithm for cyber security monitoring (CCM-RNN) was proposed, which is based on RNN and allows taking into account the dynamics of system changes in the established context, for example, the type or volume of traffic from users, etc. The method of selecting the most effective parameters and properties of ICS for detecting cyber threats was improved. The results of the study of the effectiveness of the use of the modified CCM-RNN algorithm demonstrated its broad capabilities for fast and accurate detection of anomalies in the operation of ICs that may threaten their cyber security. By changing the number of properties of the CCM-RNN algorithm, which correspond to the characteristics of various aspects of the IC, it is possible to achieve the maximum accuracy of cyber threat detection. The modified algorithm also allows for the reduction of the duration of calculations during analysis. Based on the research results, a conclusion was made about the feasibility of using the proposed modified CCM-RNN algorithm for the ability to detect cyber security threats in ICS by flexibly adjusting the number and type of learning parameters of neural networks. In this way, the accuracy and duration of calculations were optimized, as well as the peculiarities and contexts of information and communication systems were taken into account.

Design tactics for tailoring transformer architectures to cybersecurity challenges

In the rapidly evolving landscape of cyber threats, effective defense strategies are crucial for safeguarding sensitive information and critical systems. Deep learning methods, notably the Transformer architecture, have shown immense potential in addressing cybersecurity challenges. However, customizing, and adapting Transformer architectures for cybersecurity applications presents a challenge, demanding the utilization of effective strategies to achieve optimal performance. This study presents a comprehensive analysis of design tactics employed in tailoring Transformer architectures specifically for cybersecurity problems. Design tactics, defined as strategic solutions to architectural challenges based on well-justified design decisions, are explored in-depth within the context of cybersecurity. By examining the modifications and adaptations made to the original Transformer architecture, this study unveils the design decisions and strategies crucial for successful implementation in diverse cybersecurity domains. The findings emphasize the significance of aligning design tactics with the unique business requirements and quality factors of each specific application domain. This study contributes valuable insights into the utilization of design tactics for customizing Transformer architectures in cybersecurity, paving the way for enhanced defense strategies against the dynamic and evolving nature of cyber threats.

Ensuring cybersecurity of FPGA as a service with the use of penetration testing of components

The subject of study in this article is modern penetration testing technologies, in which the test object is the implementation of a service based on a platform using Field Programmable Gate Array (FPGA) resources. The goal of this study is to improve modern methods of penetration testing of services provided by FPGA as a Service (FaaS) to find vulnerabilities for further fixing and increasing the level of services security and trust. Task: to analyze the technological capabilities for the development of FPGA as a Service; to analyze possible threats for FPGA as a Service platform; to analyze the structure of the FPGA as a Service platform and the peculiarities of attacks on it; to analyze options for using the penetration testing standard; to propose the classification of possible use of FPGA as a Service platform for solving of cybersecurity tasks; and to propose the sequence of critical components of ensuring of the cybersecurity of FPGA as a Service platform. The following results were obtained based on the tasks. The analysis of the capabilities of existing chips, FPGA accelerator cards, programming technologies, and the integrated environments of a leading company for creation of FPGA as a Service is performed. A study on the cybersecurity problems of FPGA as a Service platforms is conducted, and a set of components to ensure the cybersecurity of FPGA as a Service Platform is proposed. Modern cybersecurity threats of FPGA as a Service platforms are analyzed. A threat structure for FPGA as a Service is proposed. The possibility of applying a penetration testing standard to FPGA services is considered. Regular audits and penetration testing are crucial elements of a cybersecurity strategy and help maintain customer and user trust in FPGA services. Based on the analysis of the possible use of FPGA as a Service to solve cybersecurity tasks, a classification of five variants considering FPGA as an object and tool is proposed. The sequence of critical components of ensuring of the cybersecurity of FPGA as a Service platform is proposed to correspond to modern known threats. Complex activities, including the software updates, security monitoring, auditing, and penetration testing, based on security standards. Conclusions. The primary contribution and scientific novelty of the obtained results is the research into the possibilities of penetration testing for services, where the test object is a platform with access to FPGA. As in many other areas, ensuring the cybersecurity of FPGA as a Service platform is complex, and ignoring any component can lead to critical consequences. Applying only penetration testing is not enough; therefore, a comprehensive list of cybersecurity measures for FPGA as a Service platforms is provided, underlining the urgency and necessity of their implementation.

Understanding the Multifaceted Threats of Cyber security: A Comprehensive Analysis

Cyber security exploration has significantly increased to give better protection for cyber operations and help significant security problems that may arise. The primary end of this exploration is to identify and understand common cyber security problems. In a recent study, experimenters examined 69 different studies to determine which types of security problems are constant and how severe they are. The experimenters anatomized colourful aspects similar to where the studies were published, which countries conducted the exploration, and the types of effects that were targeted by cyber-attacks. The findings of the study revealed that the current strategies used for cyber-security are veritably general, and the results they give bear further testing and real-world exploration to estimate their effectiveness.

Artificial intelligence in the sphere of cybersecurity: innovations, challenges, and development prospects

The relevance of the study is due to the need for effective protection of information systems in the face of ever-growing cyber threats. In the modern digital world, where cyberattacks are becoming increasingly complex and widespread, the use of artificial intelligence in cybersecurity is extremely relevant. The problem of cybersecurity is becoming increasingly important and conducting relevant research is becoming a necessary aspect of ensuring security in the digital space.In this context, we propose an article that examines the role of artificial intelligence, innovations, challenges, and prospects of its development in the sphere of cybersecurity. Additionally, it explores the utilization of artificial intelligence for automating processes of detecting, analysing, and responding to cyber threats, and its practical significance for enhancing security levels in the digital space and the effectiveness of cyber threat protection. The main approaches for this study are trend analysis and expert assessments, which allow for a comprehensive review of the directions of artificial intelligence development in the field of cybersecurity, the benefits and challenges of its implementation, as well as potential opportunities and threats to information security. The article discusses advanced security information and event management systems designed to collect, analyse and respond to cyber threats and other systems aimed at specific aspects ofprotection and expanding automation capabilities to improve the security level of information systems. The materials of the article are of theoretical value for further scientific research on the use of artificial intelligence in the sphere of cybersecurity and reflect important features of its influence on this area. This research can contribute not only to deepening the understanding of the theoretical aspects of the use of artificial intelligence in cybersecurity but also to identifying specific practical applications and innovations that will help improve protection against cyber threats in the future.

Cybersecurity of autonomous vehicles – threats and mitigation

The aim of this article is to draw attention to the growing problem of cybersecurity in the field of autonomous vehicles. A notable aspect is the use of autonomous vehicles to enhance the quality of decision-making processes as well as flexibility and efficiency. The implementation of new solutions will lead to improvements not just in transportation and delivery, but also in warehouse management. The growing demand for autonomous solutions, both in the industry and in the daily life of an average consumer, necessitates efforts to ensure their safe operation and use. The present literature review synthetically describes the history of the development of autonomous vehicles and machines. The standards and norms that should be met by products allowed for use as well as threats to cybersecurity, along with examples, are presented herein. The analysis of the collected materials leads to the conclusion that with the development of new technologies and the growth in the importance of autonomous solutions, the number of threats and the importance of systems securing the functioning of devices in cyberspace are increasing. Research on the problem also leads to the conclusion that legal systems do not fully keep up with technological developments, resulting in a lack of normative acts regulating this matter.

CYBER TECHNOLOGIES IN MODERN INTERNATIONAL CONFLICTS AND METHODS OF ENSURING CYBERSECURITY

The article examines the problems of cybersecurity of the state. As you know, the number and scale of international conflicts have increased recently. The methods of conducting armed conflicts have also changed. More and more people began to move from conventional military clashes to information wars and cyber wars. Over time, technological progress has led to the emergence of new arenas for conflicts between countries and factions. Cyberwar has become one of these arenas, where the main weapons are not missiles and submachine guns, but computer codes and supercomputers. This article reveals the role of cyber technologies in modern international conflicts and presents what threats and opportunities they provide on the battlefield.

The Role of Artificial Intelligence in Cyber Security

Without significant automation, humans are unable to manage the volume of data and the complexity of procedures required to safeguard cyberspace. Creating software and systems with typical fixed implementations (hardwired decision-making logic) that successfully prevent security threats is difficult. This problem can be solved with machine simplicity and AI learning approaches. This study assesses whether strengthening defensive mechanisms can improve cybersecurity capabilities and offers a brief summary of artificial intelligence (AI) applications of various cybersecurity using artificial technologies. We can determine that there are currently useful applications by looking at the most recent cybersecurity software that makes use of artificial intelligence. Neural networks are their primary tool for protecting the periphery and other cybersecurity domains. Nevertheless, it was evident that some cybersecurity problems would require the application of artificial intelligence technology. For instance, thorough information is needed for strategic decision-making, and logical decision support is one of the unmet cybersecurity needs.

Modeling information processes of big data management systems to solve cybersecurity problems

Modeling information processes of big data management systems to solve cybersecurity problems

Deep Reinforcement Learning Approach for Cyberattack Detection

Recently, there has been a growing concern regarding the detrimental effects of cyberattacks on both infrastructure and users. Conventional safety measures, such as encryption, firewalls, and intrusion detection, are inadequate to safeguard cyber systems against emerging and evolving threats. To address this issue, researchers have turned to reinforcement learning (RL) as a potential solution for complex decision-making problems in cybersecurity. However, the application of RL faces various obstacles, including a lack of suitable training data, dynamic attack scenarios, and challenges in modeling real-world complexities. This paper suggests applying deep reinforcement learning (DRL), a deep framework, to simulate malicious cyberattacks and enhance cybersecurity. Our framework utilizes an agent-based model that is capable of continuous learning and adaptation within a dynamic network security environment. The agent determines the most optimal course of action based on the network’s state and the corresponding rewards received for its decisions. We present the outcomes of our experimentation with the application of DRL on a specific model, double deep Q-network (DDQN), utilizing policy gradient (PG) on three distinct datasets: NSL-KDD, CIC-IDS-2018, and AWID. Our research demonstrates that DRL can effectively improve cyberattack detection outcomes through our model and specific parameter adjustments.