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The article examines the evolution of the cyber security paradigm in the context of the ontogenesis of the economy and public finances with an emphasis on the triggers of pre-war and war-oriented institutional memory and challenges in the post-war reconstruction of Ukraine. The scientific novelty consists in a comprehensive study of historiographical processes in cyber security and their impact on dynamic shifts in the economy and public finances, considering endo- and exogenous cyber challenges and opportunities for digital transformation and all-encompassing digitalization formed by the new economic reality. It was found that the emergence and strengthening of the role of artificial intelligence allows for a closer level of correlation between trends in the development of the economy and public finances by adding new data to predictive models, bringing them to the level of big data, identifying threats and risks in them for the development of adaptive anti-crisis-colored regulatory behavioral models for the State regulators, including in terms of creating formal and informal institutional rules for countering cyber threats. On the basis of this, practical recommendations on increasing the efficiency and productivity of the level of cyber security were formed, which correspond to the existing strategic document in this area – the Cyber Security Strategy of Ukraine, which stitches together niche strategic documents (such as the Plan for the Ukraine Facility, the Strategy for the Reform of the State Finance Management System for 2022–2025) by a single cyber defense system.

Purpose. Taking into account the existing threats to the foundations of the national and military security of Ukraine and in the conditions of rapidly changing geopolitical realities, there is a need to improve the existing criminal policy of the state, as well as some provisions of criminal legislation in the field of proper protection of such objects of criminal law protection against cyber attacks. Methodology. The methodological basis of the research is a set of general scientific principles and approaches, philosophical-worldview and special-scientific methods of learning socio-legal processes and phenomena, including: historical, dogmatic, comparative-legal, formal-logical, systemic-structural, modeling method. Results. In the process of research, it was established the need to improve a number of provisions of the Special Part of the Criminal Code of Ukraine, which will allow strengthening measures of criminal repression in relation to anti-state offenses carried out through cyber attacks, and, accordingly, to reorient the methodological foundations of the existing criminal policy of our state with regard to the severity of criminal repression measures in order to localize such anti-state criminal manifestations in modern conditions. This provision allows to some extent to improve the current criminal legislation on countering cyber attacks on the bases of national and military security of Ukraine, and therefore to significantly increase the effectiveness of combating them at the current stage of such protection of the objects under consideration. Originality. In the process of research, a scientifically based concept of criminal law protection of the foundations of national and military security of Ukraine was developed, which defines new directions of the existing criminal policy of the state in this area, and also contains a number of proposals for improving some provisions of the current criminal legislation in the field of cyber security protection of Ukraine. Practical significance. The results of the research can be used in law-making and law-enforcement activities during the investigation of crimes related to the use of modern means of electronic communications, information and communication technologies, software, hardware and other technical and technological means and equipment against the foundations of national and military security of Ukraine.

The problems and directions of ensuring effective international digital information security in Central and Eastern Europe (CEE) are considered, which are based on the analysis of the current state, key trends and challenges of information security in this region, using current reporting materials of international companies specializing in information security issues and development of appropriate complex tools for its provision. The subject of research in the article is the issue of ensuring international digital information security in the CEE. The goal is to study the current state, current problems and trends of international digital information security on the example of the region of Central and Eastern Europe in order to find ways to improve its effectiveness. Task: to process and analyze current reporting related to the current state of international digital information security in the CEE region; to explore the key trends, challenges, problems and industry specificities of digital information security in CEE; analyze approaches to assessing the current state of information security of international organizations in the CEE region and directions for its improvement. The general scientific method of system analysis is used - to determine the current state, key trends and challenges of digital information security, as well as to research promising directions for improving its effectiveness. The following results were obtained: based on the processing of reports concerning the current state of international digital information security in Central and Eastern Europe, key trends, challenges and problems of digital information security in the CEE region were investigated. The sectoral features of digital information security in the spheres of financial services, production and retail trade of this region have been determined. Effective approaches to assessing the current state of information security of international organizations in the CEE have been analyzed. Possible directions for its improvement and prospects for further scientific research on this topic are determined. Conclusions: the obtained results made it possible to understand the current landscape of cyber security in Central and Eastern Europe in general. Positive impulses have been identified in the focus of companies in the region on strengthening cyber security, which requires them to have a clear orientation in the information risk management system. The biggest current problems of CEE cyber security are highlighted. It has been established that the existing concern and actual behavior regarding information security in CEE contradict each other: most international companies have a significant dissonance between the fears of information threats and the actions of their management. It was determined that one of the biggest problems of CEE organizations in ensuring information security is the low level of involvement of their managers in this process, as well as the neglect of the awareness of the employees of the organizations about security and cyber protection. Establishing a secure remote work environment remains a top priority, along with the ongoing fight against malicious malware attacks. It was determined that the use of cloud technologies remains particularly relevant for international companies and CEE institutions. The need for cooperation between organizations and external suppliers of relevant solutions in ensuring the information security of the region was revealed. It has been established that there are some unique points regarding information security issues in the industry comparison; financial services, manufacturing and retail trade stand out among such CEE special industries. An effective list of control questions regarding an adequate assessment of the current state of the organization's information security to determine potential directions for its improvement is proposed. Plans for improving the effectiveness of digital information security of organizations in the CEE region are outlined.

Phishing poses a significant challenge in an ever-evolving world. The increased usage of the Internet has resulted in the emergence of a different kind of theft referred to as cybercrime. The term cybercrime describes the act of invading privacy and illegitimately obtaining personal information using digital platform. Primarily an approach named phishing is employed, which involves the use of spoof emails or bogus websites by the attackers to get the victim's personal information like their account credentials, debit, or credit card’s number, etc. To give the brief knowledge of phishing attacks and their types of the objective of this work is to investigate various AI algorithms. Through a detail literature 14 AI algorithms which are repeatedly used for detection, and these are Random Forests, Convolutional Neural Network, Naïve Bayes, K-Nearest Neighbours algorithm, Decision Trees, long short-term memory, gated recurrent unit, Artificial Neural Network, AdaBoost, Logistic Regression, Gradient Boost, Multi-layer perceptron, Recurrent Neural Network, Extreme gradient boosting, and Support Vector Machine to detect phishing attacks. To verify the effectiveness of these algorithms an experiment is performed on two datasets. Among all the algorithms Convolutional Neural Network, Multi-layer perceptron and AdaBoost achieved more than 90% accuracy, precision and sensitivity and it was showed through results that these algorithms are very efficient and can achieve high accuracy if used to the requirements of specific scenario with proper planning. Moreover, the paper shows how different AI techniques have been employed in multiple studies to detect and address phishing attacks. Also, this paper gives a complete list of current problems with phishing attacks and ideas for future studies in this area.

Malware has become one of the biggest risks to security due to its rapid expansion. Therefore, it must be quickly detected and removed. While convolutional neural network (CNN) models have expanded to include ensemble and transfer learning approach from previous individual CNN architectures, relatively few studies have compared how well these approaches perform when it comes to malware family detection. A small number of malware varieties have been the focus of several research efforts’ studies. In this study, both of these issues were resolved. We present our own ensemble model for the classification of malware diseases into 34 types. We merge the Microsoft malware dataset with the Malimg dataset to increase the number of malware families identified by the model. To reduce training time and resource consumption, the suggested model utilized the most significant malware features, which are chosen based on the Least Absolute Shrinkage and Selection Operator method, for the purpose of classifying the malware classes. The experimental findings demonstrate that the ensemble model’s accuracy is 99.78%. Based on the experimental results, we conclude that the model will help with real-world malware classification tasks.

Abstract 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.

In the vast, unbounded domain of cyberspace, the concept of regional dynamics often seems overshadowed by its inherent global nature. However, the Eastern European digital landscape presents a compelling case for recognizing and understanding the significance of regional cybersecurity nuances. This article delves into the unique cyber threats faced by Eastern Europe, influenced by intertwined state relations, particularly the pervasive cyber influence of Russia. It highlights shared vulnerabilities resulting from common technological infrastructures, interlinked economies, and mutual dependencies that make the region a collective cyber target. Furthermore, the article discusses the external shaping forces, such as the regulatory influence of the European Union and the strategic involvement of global powers like the US, in strengthening the region’s cyber defences. By juxtaposing the global essence of cyberspace with the discernible regional contours in Eastern Europe, this article underscores the importance of a regional perspective in formulating nuanced, effective cybersecurity strategies.

Drawing from Kantianism, utilitarianism, information systems ethical models, and South Africa Department of Education policies, this study investigated how high schools can ethically provide online learning. The study was prompted by two unethical concerns highlighted in the literature: firstly, the potential discrimination to online learning against learners who do not have access to information technology resources, and secondly, the cyber risks faced by learners from prolonged exposure to Internet connected devices. To gather data for the study, interviews were conducted with 15 schoolteachers, who were conveniently sampled from five schools in Centurion, Pretoria city, South Africa. The data was thematically analysed, and the results of the study found constructs that inform ethical provision of online learning, which are: equal access to online learning, teacher competence, teacher empathy, and cyber security of learners. The findings of this study inform the policy on providing ethical online learning in South Africa and any other country.

Learning AI is used to detect various cyber-attacks, malicious attacks and unusual future threats in the network. Notice the pattern in which any illegal or suspicious activities behave, recognize what is needed, and identify proactive behaviours that indicate the presence of threats. In light of this, the two researchers provide an in depth review of research related to artificial intelligence in cyberspace and through learning techniques used to train machines to enhance cyber security. The studies that were used, the techniques, the extent of their effectiveness and efficiency in achieving the desired goal were reviewed, detailed, sorted and compared with each other, especially the data used to build the model and the diversity of these methods that were used to create its model.

The development of the post-industrial society requires the acceleration of the integration of the national economy into the globalized economic space. This stage is characterized by active informatization of all spheres of life in society, which requires information security and cyber protection for high-quality information provision of the country’s population, intellectualization of economic processes and prevention of destructive informational influence on the social status of the individual. It is also necessary to consider that for Ukraine the specified stage is complicated by the hybrid war with Russia, which requires strengthening the protection of information from cyber-attacks and the formation of new approaches in preserving the quality of information on social networks. Therefore, the purpose of this article is the development of scientific and methodological approaches in information security management, strengthening its social significance. This requires solving a certain range of tasks of identifying and preventing socially dangerous information based on the use of economic and mathematical methods and models. The article highlights the main directions of socialization of modern technological development and theoretically substantiates its significant impact on human consciousness and behavior, puts a person in front of serious challenges and, under conditions of hybrid warfare, requires strengthening of information security in social networks. In the work, the components of information security are supplemented, its impact on social aspects of society’s life is highlighted. As a result of the research, the authors proposed a scientific and methodical approach to the construction of a system for countering the spread of socially dangerous information in social networks. Besides, its functional elements are highlighted. A method of combating the spread of harmful information in social networks has been developed, which solves the problem of information support of the decision-making process and includes providing the chosen and alternative options to the person making the decision, with the justification of the choice. This creates prerequisites for information support for making a well-founded management decision.

The smart shipping and maritime technology encompass blockchain and smart contracts technology, information perception technology, intelligent decision-making technology, intelligent routing technology, marine communication network security technology, route planning technology, autonomous navigation technology, state monitoring and fault diagnosis technology and so on. Nevertheless, these advancements bring practical and legal challenges, as well as a new threat in the digital age: cybercrime. This paper deals with the key challenges and opportunities associated with the integration of digital technologies in shipping and transport such as smart contracts and unmanned ships, and how do cyber issues impact the safety, security, and efficiency of maritime operations. The first part examines the technological background into which smart contracts are integrated and operate, i.e. the terms blockchain and technology distributed ledger technology. The concept, the mechanism and the types of smart contracts in the maritime industry are further analysed. Also, the aspects of new technologies such as Autonomous Vessels and the challenges they raise are examined. The second part focuses on the legal potential of smart contracts examining their issues and the cyber challenges in the maritime industry. The critical remarks and conclusions drawn are listed at the end of the paper.

Cloud computing has become integral to modern computing infrastructure, offering scalability, flexibility, and cost-effectiveness. Trust is a critical aspect of cloud computing, influencing user decisions in selecting Cloud Service Providers (CSPs). This paper provides a comprehensive review of existing trust models in cloud computing, including agreement-based, SLA-based, certificate-based, feedback-based, domain-based, prediction-based, and reputation-based models. Building on this foundation, we propose a novel methodology for creating a trust model in cloud computing using digital twins for CSPs. The digital twin is augmented with a fuzzy inference system, which computes the trust score of a CSP based on trust-related parameters. The architecture of the digital twin with the fuzzy inference system is detailed, outlining how it processes security parameter values obtained through penetration testing mechanisms. These parameter values are transformed into crisp values using a linear ridge regression function and then fed into the fuzzy inference system to calculate a final trust score for the CSP. The paper also presents the outputs of the fuzzy inference system, demonstrating how different security parameter inputs yield various trust scores. This methodology provides a robust framework for assessing CSP trustworthiness and enhancing decision-making processes in cloud service selection. [ABSTRACT FROM AUTHOR]

The healthcare industry is increasingly reliant on access to large volumes of patient data, which is critical for improving patient outcomes, reducing costs, and ensuring regulatory compliance. However, there are significant challenges facing healthcare organizations in retrieving patient data from databases and ensuring the security and privacy of this data. Addressing these challenges is vital to the future success of the healthcare industry. This paper proposes two solutions to these challenges: a simplified ontology-based retrieval algorithm for healthcare multimedia data to enhance the retrieval process, and Hierarchical learning: ensemble model-based reinforcement learning to improve patient data security. An ontology-based retrieval algorithm was a method used to retrieve relevant information from large datasets. It filtered out irrelevant data and provided contextually relevant results. Hierarchical learning involved organizing learning tasks in a hierarchical structure, with higher-level policies guiding lower-level policies. Ensemble model-based reinforcement learning combined multiple models to improve the performance of a reinforcement learning system. The Simplified Ontology-Based Retrieval Algorithm had excellent performance with an accuracy of 92%, making it highly effective in information retrieval tasks. The paper highlights the importance of using advanced technologies and prioritizing cyber security in healthcare to maintain trust and optimal patient care. Cutting-edge technologies transformed healthcare by enhancing patient data retrieval and cyber security. Examples included encrypted computations, multi-party privacy, query result anonymization, real-time monitoring through IoMT, and secure data sharing. Adopting these solutions ultimately contributed to the protection of sensitive patient data and the overall security of healthcare organizations. [ABSTRACT FROM AUTHOR]

The object of this study is a disinformation detection process based on search algorithms for identifying fake news. The main task was to define a set of criteria and parameters for detecting the Ukrainian-language disinformation based on machine learning. A methodology has been considered for developing and filling a dataset of fakes for further training of the model and testing it for the purpose of identifying disinformation and propaganda, as well as determining the attributes of primary sources and routes of their distribution. This makes it possible to reasonably approach the definition of a model for forecasting the development of information threats in the cyberspace of Ukraine. In particular, the accuracy of automatic detection of the probability of disinformation in texts can be increased. For the English-language texts using balanced datasets for training when applying classical machine learning classifiers, the accuracy of identification and recognition of fakes is ≥90 %, and for the Ukrainian-language texts – ≥52 % and ≤90 %. That has made it possible to devise requirements for the structure and content of a typical dataset of fakes in the period after the full-scale invasion of Ukraine. The practical result of this work is the designed decisionmaking support system for monitoring, detecting, recognizing, and forecasting information threats in the cyberspace of Ukraine based on NLP and machine learning. The implementation of preliminary processing of the Ukrainian-language news, taking into account the linguistic features of the language in the text, increases the accuracy of fake identification by ≈1.72 times. Approaches to the construction of models for forecasting the development of information threats in cyberspace have been developed, which is an urgent task when fake news and information manipulation can affect public sentiment, politics, and the economy [ABSTRACT FROM AUTHOR]

The growing complexity of security threats and the pervasive prevalence of cyberattacks have become more apparent in the present era, and the advent of big data, characterized by its distinctive features, has introduced layers of complexity to security tasks. Intrusion Detection Systems (IDSs) constitute a crucial line of defense, but their adaptation to the realm of big data is imperative. While traditional Machine Learning (ML)-based IDSs have been pivotal in detecting malicious patterns, they are often incapable to keep pace with the demands of expansive big data networks. This paper proposes a novel decentralized Multi-Agent Reinforcement Learning (MARL)-based IDS designed to address the specific challenges posed by big data. Our solution employs decentralized cooperative MARL, securing communicative channels throughout the detection process and concurrent data preprocessing which significantly reduces the overall processing time. Furthermore, the integration of Cloud computing and Big Data streaming techniques further facilitates real-time intrusion detection as cloud's resources allow rapid pre-process and analyse of massive data streams using powerful clusters. Likewise, Big Data streaming techniques ensure that potential intrusions are identified and addressed as they occur. Experimental results, conducted on the widely recognized NSLKDD benchmark dataset, demonstrate the superiority of our solution over other state-of-the-art approaches for big data networks, achieving an accuracy rate of 97.44%. [ABSTRACT FROM AUTHOR]

The focus of this review is the long and broad history of attacker–defender games as a foundation for the narrower and shorter history of cyber security. The purpose is to illustrate the role of game theory in cyber security and which areas have received attention and to indicate future research directions. The methodology uses the search terms game theory, attack, defense, and cyber security in Web of Science, augmented with the authors' knowledge of the field. Games may involve multiple attackers and defenders over multiple periods. Defense involves security screening and inspection, the detection of invaders, jamming, secrecy, and deception. Incomplete information is reviewed due to its inevitable presence in cyber security. The findings pertain to players sharing information weighted against the security investment, influenced by social planning. Attackers stockpile zero-day cyber vulnerabilities. Defenders build deterrent resilient systems. Stochastic cyber security games play a role due to uncertainty and the need to build probabilistic models. Such games can be further developed. Cyber security games based on traffic and transportation are reviewed; they are influenced by the more extensive communication of GPS data. Such games should be extended to comprise air, land, and sea. Finally, cyber security education and board games are reviewed, which play a prominent role. [ABSTRACT FROM AUTHOR]

Email bombing, a growingly prevalent and destructive cyber attack, involves inundating a target's email inbox with subscription confirmation messages from legitimate services. This type of attack, particularly challenging for conventional spam filters, is not easily detected as the emails often appear benign. In our research, we introduce an innovative real-time technique to identify and mitigate email bombing. Our method leverages a unique time gap threshold for attack detection, proving effective across various bombing types, including mass mailing and subscription bombing. Upon detecting an attack, we utilize a novel mechanism of nine features extracted from email headers to build a machine learning model. This model distinguishes between genuine and bombing emails with approximately 97% accuracy. Our study not only explores email bombing attacks but also offers a comprehensive solution, combining attack detection and a machine learning-based approach to accurately classify emails, thereby effectively mitigating such cyber threats. [ABSTRACT FROM AUTHOR]

Increasing numbers of healthcare data breaches highlight the need for structured organisational responses to protect patients, trainees and psychiatrists against identity theft and blackmail. Evidence-based guidance that is informed by the COVID-19 pandemic response includes: timely and reliable information tailored to users' safety, encouragement to take protective action, and access to practical and psychological support. For healthcare organisations which have suffered a data breach, insurance essentially improves access to funded cyber security responses, risk communication and public relations. Patients, trainees and psychiatrists need specific advice on protective measures. Healthcare data security legislative reform is urgently needed. [ABSTRACT FROM AUTHOR]

This community service activity (PKM) was held to organize socialization of cybersecurity awareness and personal data protection. Through this socialization activity, it is hoped that it can help the community in dealing with actual situations and conditions related to cyber threats and data leaks that are currently rampant. This Community Service Activity is aimed at residents of Pangkalan Jati Village, Depok City. This activity aims to provide socialization and understanding of the importance of cybersecurity awareness and personal data protection for all levels of society. Supported by a good situation and conditions, this activity was held face-to-face directly at the Pangkalan Jati Village Hall. The number of participants who attended was around 30 people consisting of community leaders, RT Heads, and the younger generation in the Pangkalan Jati Village area, Depok City. This socialization explained material regarding the urgency of cybersecurity awareness, various regulations and policies related to cyber and personal data protection, to various tips for protecting each person's personal data. Moreover, in the context of the 2024 election which has just been implemented, it can be a challenge in itself with the massive developments in cyberspace. Through this activity, it can be concluded that this activity is important to be held routinely because there are still many people who do not understand the urgency of cyber security. [ABSTRACT FROM AUTHOR]

The number of connected devices or Internet of Things (IoT) devices has rapidly increased. According to the latest available statistics, in 2023, there were approximately 17.2 billion connected IoT devices; this is expected to reach 25.4 billion IoT devices by 2030 and grow year over year for the foreseeable future. IoT devices share, collect, and exchange data via the internet, wireless networks, or other networks with one another. IoT interconnection technology improves and facilitates people's lives but, at the same time, poses a real threat to their security. Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks are considered the most common and threatening attacks that strike IoT devices' security. These are considered to be an increasing trend, and it will be a major challenge to reduce risk, especially in the future. In this context, this paper presents an improved framework (SDN-ML-IoT) that works as an Intrusion and Prevention Detection System (IDPS) that could help to detect DDoS attacks with more efficiency and mitigate them in real time. This SDN-ML-IoT uses a Machine Learning (ML) method in a Software-Defined Networking (SDN) environment in order to protect smart home IoT devices from DDoS attacks. We employed an ML method based on Random Forest (RF), Logistic Regression (LR), k-Nearest Neighbors (kNN), and Naive Bayes (NB) with a One-versus-Rest (OvR) strategy and then compared our work to other related works. Based on the performance metrics, such as confusion matrix, training time, prediction time, accuracy, and Area Under the Receiver Operating Characteristic curve (AUC-ROC), it was established that SDN-ML-IoT, when applied to RF, outperforms other ML algorithms, as well as similar approaches related to our work. It had an impressive accuracy of 99.99%, and it could mitigate DDoS attacks in less than 3 s. We conducted a comparative analysis of various models and algorithms used in the related works. The results indicated that our proposed approach outperforms others, showcasing its effectiveness in both detecting and mitigating DDoS attacks within SDNs. Based on these promising results, we have opted to deploy SDN-ML-IoT within the SDN. This implementation ensures the safeguarding of IoT devices in smart homes against DDoS attacks within the network traffic. [ABSTRACT FROM AUTHOR]

The smart grid is a promising solution for modernizing electrical power systems in a digital world. However, cybersecurity threats pose significant risks to the grid's integrity and resilience. False Data Injection Attacks (FDIA) have shown serious risks to disrupt the power system infrastructure. SHASARIMAX is a novel framework designed to address these challenges by combining distinct elements of Secure Hash Algorithm-256 (SHA256) and SARIMAX. Cryptographic hashing techniques and advanced modelling of SHA-SARIMAX actively detect and recover corrupted values in time series datasets affected by FDIA. This approach detects and recovers corrupted values in time series datasets affected by FDIA, leveraging cryptographic hashing techniques and advanced modelling. The proposed framework achieves a 99.90% accuracy rate from the original corrupted data. Thus, the SHA-SARIMAX demonstrates its applicability and suitability for implementation within smart grids, specifically for addressing the recovery of original data from the corrupted data affected by FDIA. [ABSTRACT FROM AUTHOR]

As the number and cleverness of cyber-attacks keep increasing rapidly, it's more important than ever to have good ways to detect and prevent them. Recognizing cyber threats quickly and accurately is crucial because they can cause severe damage to individuals and businesses. This paper takes a close look at how we can use artificial intelligence (AI), including machine learning (ML) and deep learning (DL), alongside metaheuristic algorithms to detect cyber-attacks better. We've thoroughly examined over sixty recent studies to measure how effective these AI tools are at identifying and fighting a wide range of cyber threats. Our research includes a diverse array of cyberattacks such as malware attacks, network intrusions, spam, and others, showing that ML and DL methods, together with metaheuristic algorithms, significantly improve how well we can find and respond to cyber threats. We compare these AI methods to find out what they're good at and where they could improve, especially as we face new and changing cyber-attacks. This paper presents a straightforward framework for assessing AI Methods in cyber threat detection. Given the increasing complexity of cyber threats, enhancing AI methods and regularly ensuring strong protection is critical. We evaluate the effectiveness and the limitations of current ML and DL proposed models, in addition to the metaheuristic algorithms. Recognizing these limitations is vital for guiding future enhancements. We're pushing for smart and flexible solutions that can adapt to new challenges. The findings from our research suggest that the future of protecting against cyber-attacks will rely on continuously updating AI methods to stay ahead of hackers' latest tricks. [ABSTRACT FROM AUTHOR]

In recent years, there has been a noticeable trend toward targeted threats to information security, where companies are now leveraging vulnerabilities and risks associated with widely used services in order to generate financial gain. Additionally, they implement numerous precautions and consistently carry out their tasks. One item that requires precautionary measures is the network devices utilized. Network devices in computer networks possess the capability to log events. These logs enable the identification of security events on the network and facilitate the implementation of precautionary measures. Various security measures can be implemented to handle such data. One of these measures is Security Information and Event Management (SIEM). It is a system that gathers and analyzes data from networks and security devices. SIEM is a technique employed to consolidate critical information within a cohesive structure. It allows for the correlation of events from different security devices, thereby improving the monitoring capabilities of cybersecurity operations centers. This study extensively covers the critical infrastructure-SIEM relationship, current studies, critical infrastructure, cyber security policies, and SIEM. Our system design was developed using the UNSW NB15 dataset, a widely recognized dataset in cybersecurity due to its comprehensive and realistic representation of cyber threats. This dataset consists of data obtained from network traffic, various attack activities, and real-life modern normal scenarios, making it particularly relevant to our study. With the studies, a total of 10 different categories were analyzed, with the category consisting of nine types of attacks, namely Analysis, Backdoor, DoS, Exploits, Fuzzers, Generic, Reconnaissance, Shellcode, and Worms and Normal activities. The study is divided into two as the basic structure. The first step was carried out on Google Collaboratory, and then some experimental studies were carried out in Weka. Classifications were made using several methods, including Logistic Regression (LR), Extra Trees (XT), Support Vector Machines (SVM), Random Forest (RF), and Decision Trees (DT). These methods were chosen for their proven effectiveness in similar studies. In the application developed with Google Colabratory, we achieved 98.62% in Random Forest, 99.10% in Decision Trees, 98.87% in Logistic Regression, 95.13% success in Extra Trees and 99.12% success in Support Vector Machines. As a result of the studies and experiments carried out in Weka, we achieved 92.05% in Random Forest, 100% in Decision Trees, 100% in k-Nearest Neighbours, 100% in J48, 99.19% in Naive-Bayes and 99.35% in BayesNet achievements. [ABSTRACT FROM AUTHOR]

Increasing internet consumption and constant technological development, specifically smartphones, have led to an increasingly urgent need for cyber awareness. Generally, smartphone cyber security is an established field and has inspired numerous relevant studies. Unfortunately, most past studies lacked focus on cyber security research in the form of a systematic literature search of smartphone use by the worldwide community. Therefore, this study reviewed past studies on mitigating cyber security risk in highly technology-dependent communities using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines and the Web of Science, Scopus, ScienceDirect, and Google Scholar online databases. The search strategy yielded 23 articles in total for inclusion in the systematic analysis. The thematic analysis yielded three themes: awareness, knowledge, and behavior, which in turn revealed 10 sub-themes. Finally, recommendations were suggested for reference by future scholars. [ABSTRACT FROM AUTHOR]

As technology advances, the frequency of attacks targeting technological devices has surged. This rise in cyber threats poses a constant risk to the devices we rely on. Any device connected to a network becomes vulnerable to exploitation by attackers. Given the extensive interconnectedness of devices in network environments, this research endeavors to address this pressing issue. The aim of this study is to analyze and classify network traffic generated during potential cyber attacks using various classification algorithms. By subjecting a simulated environment to different cyber attack scenarios, we extract the distinctive features of network packets generated during these attacks. Subsequently, we employ widely used classification algorithms to train and analyze the obtained data. For the comparison of models, more than 7000 attack data instances were employed. At the conclusion of the comparison, the Gradient Boosting algorithm achieved the highest accuracy value, reaching 91%, whereas the Naive Bayes algorithm obtained the lowest accuracy, reaching 74%. [ABSTRACT FROM AUTHOR]

The abstract highlights the critical need for securing sensitive medical data, emphasizing the challenges in the medical domain due to the confidentiality of patient, disease, doctor, and staff information. The proposed study introduces a novel approach using machine learning, specifically integrating the firefly optimization technique with the random forest algorithm, to classify medical data in a secure manner. The significance lies in addressing the security concerns associated with medical datasets, offering a solution that prioritizes confidentiality throughout the software development life cycle (SDLC). The proposed algorithm achieves an impressive accuracy of 96%, showcasing its efficacy in providing a robust and secure framework for the development of applications involving medical data. This research contributes to advancing the field of medical data security, offering a practical solution for safeguarding sensitive information in healthcare applications. [ABSTRACT FROM AUTHOR]

The increase in number of people using the Internet leads to increased cyberattack opportunities. Advanced Persistent Threats, or APTs, are among the most dangerous targeted cyberattacks. APT attacks utilize various advanced tools and techniques for attacking targets with specific goals. Even countries with advanced technologies, like the US, Russia, the UK, and India, are susceptible to this targeted attack. APT is a sophisticated attack that involves multiple stages and specific strategies. Besides, TTP (Tools, Techniques, and Procedures) involved in the APT attack are commonly new and developed by an attacker to evade the security system. However, APTs are generally implemented in multiple stages. If one of the stages is detected, we may apply a defense mechanism for subsequent stages, leading to the entire APT attack failure. The detection at the early stage of APT and the prediction of the next step in the APT kill chain are ongoing challenges. This survey paper will provide knowledge about APT attacks and their essential steps. This follows the case study of known APT attacks, which will give clear information about the APT attack process—in later sections, highlighting the various detection methods defined by different researchers along with the limitations of the work. Data used in this article comes from the various annual reports published by security experts and blogs and information released by the enterprise networks targeted by the attack. [ABSTRACT FROM AUTHOR]

The constantly changing nature of cyber threats presents unprecedented difficulties for people, institutions, and governments across the globe. Cyber threats are a major concern in today's digital world like hacking, phishing, malware, and data breaches. These can compromise anyone's personal information and harm the organizations. An intrusion detection system plays a vital responsibility to identifying abnormal network traffic and alerts the system in real time if any malicious activity is detected. In our present research work Artificial Neural Networks (ANN) layers are optimized with the execution of Spider Monkey Optimization (SMO) to detect attacks or intrusions in the system. The developed model SMO-ANN is examined using publicly available dataset Luflow, CIC-IDS 2017, UNR-IDD and NSL -KDD to classify the network traffic as benign or attack type. In the binary Luflow dataset and the multiclass NSL-KDD dataset, the proposed model SMO-ANN has the maximum accuracy, at 100% and 99%, respectively. [ABSTRACT FROM AUTHOR]

This study investigates the intricate relationship between ideological and political education (IPE), cyber security, and digital literacy within the context of online information warfare, aiming to shed light on strategies for navigating the digital divide. Utilizing a quantitative research approach and convenience sampling, data were collected through a meticulously crafted online survey from a diverse range of participants, including students, professionals, and other individuals. Analysis of the data, conducted using SPSS software and Smart-PLS 3, revealed several significant findings. Firstly, there exists a pronounced disparity in access to diverse online information based on individuals' levels of IPE. Additionally, media literacy skills exhibit significant variation across different levels of IPE. Furthermore, the study elucidates how IPE influences individuals' propensity to engage in online echo chambers and their susceptibility to cyber security vulnerabilities. Finally, it underscores the role of IPE in shaping digital participation and civic engagement. Overall, this research contributes novel insights into the complex dynamics of online information warfare and highlights the pivotal role of ideological and political education in mitigating the digital divide. This study underscores the critical importance of IPE in shaping individuals' digital literacy, cyber security awareness, and engagement with online information environments. By elucidating the impact of IPE on navigating the digital divide, this research contributes valuable insights for policymakers, educators, and stakeholders seeking to foster a more informed, resilient, and inclusive digital society. [ABSTRACT FROM AUTHOR]

Electric vehicles (Evs) are increasingly taking center stage in efforts to reduce emissions and dependence on fossil fuels. However, the critical challenges of optimizing battery performance and maintaining cybersecurity are the focus. In this study, we conducted a systematic literature review to explore the key role of artificial intelligence (AI) in addressing these challenges. This research includes answers to several research questions such as the utilization of AI in Hybrid EVs, the role of AI in estimating battery life, cyber security in EVs, and several things related to battery optimization using AI. Through a careful synthesis of the literature, we highlight various AI in Hybrid EVs is frequently employed for Battery Management Systems. AI can also play a role in battery charging for EVs based on various applied methods. We have found that cyber-attacks on EVs are not much different from those on websites/applications, and EV Charging Stations can also be targeted for cyber-attacks. The utilization of blockchain-based smart contracts can address security issues during the charging process. In estimating battery life, we found that the Ensemble Regression method could be considered the most optimal, based on comparisons with several studies we encountered. Our results illustrate future research directions in the face of the complexity of EV technology and offer a deeper look into the potential of artificial intelligence integration to improve EV performance and safety. [ABSTRACT FROM AUTHOR]

The volume of data transferred across communication infrastructures has recently increased due to technological advancements in cloud computing, the Internet of Things (IoT), and automobile networks. The network systems transmit diverse and heterogeneous data in dispersed environments as communication technology develops. The communications using these networks and daily interactions depend on network security systems to provide secure and reliable information. On the other hand, attackers have increased their efforts to render systems on networks susceptible. An efficient intrusion detection system is essential since technological advancements embark on new kinds of attacks and security limitations. This paper implements a hybrid model for Intrusion Detection (ID) with Machine Learning (ML) and Deep Learning (DL) techniques to tackle these limitations. The proposed model makes use of Extreme Gradient Boosting (XGBoost) and convolutional neural networks (CNN) for feature extraction and then combines each of these with long short-term memory networks (LSTM) for classification. Four benchmark datasets CIC IDS 2017, UNSW NB15, NSL KDD, and WSN DS were used to train the model for binary and multi-class classification. With the increase in feature dimensions, current intrusion detection systems have trouble identifying new threats due to low test accuracy scores. To narrow down each dataset's feature space, XGBoost, and CNN feature selection algorithms are used in this work for each separate model. The experimental findings demonstrate a high detection rate and good accuracy with a relatively low False Acceptance Rate (FAR) to prove the usefulness of the proposed hybrid model. [ABSTRACT FROM AUTHOR]

According to theoretical and empirical knowledge, cybersecurity awareness is a crucial issue in cyber security. The main actors in cyber security are people, and one way to reduce risk in cyberspace is to increase knowledge of security concerns. Companies lose money as a result of data breaches and production losses brought on by cyberattacks. Consequently, there has been a surge in research endeavours aimed at comprehending the cybersecurity behaviours of users. The benefit of knowing user behaviours is that researchers and security professionals may utilize this information to start altering behaviours for the sake of cybersecurity. Similar cybersecurity behaviours have been categorized by several research, while the naming systems used vary. Sanctions, a decline in customer loyalty, and damage to one's brand may all arise from data breaches. Business continuity is also impacted by cyberattacks, which make it difficult for organizations to maintain constant production. This paper aims to demonstrate that, in addition to computer science research, behavioural sciences that study user behaviours can offer useful strategies to improve cyber security and lessen the impact of attackers' social engineering and cognitive hacking tactics (i.e., disseminating misleading information). Thus, in this study, we provide fresh insights on the psychological characteristics and individual variances of computer system users that account for their susceptibility to cyberattacks and crimes. Our investigation shows that different computer system users have different cognitive capabilities, which affects their ability to defend against information security threats. In order to improve network and information security, we identify research gaps and suggest possible psychological techniques to help computer system users follow security requirements. [ABSTRACT FROM AUTHOR]

The Internet is often described as a unique tool, unique platform, unique experience - but what is the Internet for? One might get caught in an increasing population of criminals who master the social web brilliantly. Cyberspace or the Web is an environment that is ethereal and dynamic. Assuming that the claim of this paper is true, cybercrime or e-crime is characterized as a new form of criminality perpetrated by technologically sophisticated criminals. This paper aims at describing cybercrimes by analyzing the actors involved in the commission of these acts and the reasons prompting such actions. That is why it also encompasses different categories of cybercriminal activities and outlines their peculiarities, which may concern the prevention, identification, and investigation of the respective crimes. Furthermore, the paper outlines the key parts of the Indian IT Act, 2000 besides offering recommendations as to possible changes. [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]

Internet is being actively used world over. Covid 19 pandemic have brought a steep rise in internet use by users of all age groups. In India specifically, there are total 692 million active internet users that includes 351 million users from rural India and 341 from urban India. Without a doubt, the advantages of internet use are numerous. It is but obvious that with such a high rate of internet use, online world also opens up platforms for ever increasing cybercrimes in country. Increased online time, lack of awareness among internet users are popular reasons for the increased cybercrime trends. Moreover, lack of technical knowledge among professionals adds to the complications in solving cybercrime cases. Hackers, on the other hand, are coming up with new and innovative modus operandi to scam more and more people. This paper analyses the cybercrime related news content of leading news websites in India. The research is conducted to investigate cybercrime news reporting, identify the increase in cybercrimes and to understand the types of cybercrimes taking place in the country. It attempts to understand the most popular strategies being adopted by online attackers for luring online users. This paper attempts to create necessary mindfulness while using the internet. It is analyzed that cybercrime awareness campaigns of government are unable to make an impact at a larger level. [ABSTRACT FROM AUTHOR]

The major component of the smart grid (SG) is the advanced metering infrastructure (AMI), which monitors and controls the existing power system and provides interactive services for invoicing and electricity usage management with the utility. Including a cyber-layer in the metering system allows two-way communication but creates a new opportunity for energy theft, resulting in significant monetary loss. This article proposes an approach to detecting abnormal consumption patterns using energy metering data based on the ensemble technique AdaBoost, a boosting algorithm. Different statistical and descriptive features are retrieved from metering data samples, which account for extreme conditions. The model is trained for malicious and non-malicious data for five different attack scenarios, which are analyzed on the Irish Social Science Data Archive (ISSDA) smart meter dataset. In contrast to prior supervised techniques, it works well even with unbalanced data. The efficacy of the proposed theft detection method has been evaluated by comparing the accuracy, precision, recall, and F1 score with the other well-known approaches in the literature. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) refers to a structure where multiple devices and sensors communicate with each other over a network. As the number of internet-connected devices increases, so does the number of attacks on these devices. Therefore, it has become important to secure the data and prevent potential threats to the data in factories or workplaces. In this study, a deep learning-based architecture was used to determine whether the data collected from IIoT sensors was under attack by looking at network traffic. The data that was not exposed to attacks was stored on the Ethereum Blockchain network. The Ethereum blockchain network ensured that sensor data was stored securely without relying on any central authority and prevented data loss in case of any attack. Thanks to the communication process over the blockchain network, updating and sharing data was facilitated. The proposed deep learning-based intrusion detection system separated normal and anomaly data with 100% accuracy. The anomaly data were identified with an average of 95% accuracy for which attack type they belonged to. The data that was not exposed to attacks was processed on the blockchain network, and an alert system was implemented for the detected attack data. This study presents a method that companies can use to secure IIoT sensor data. [ABSTRACT FROM AUTHOR]

Purpose. Analysis of the role of artificial intelligence (AI) in ensuring the cyber security of military networks and the possibility of applying AI in cybersecurity in defense. Methodology. The research used methods of analysis of scientific and literary sources, information search, comparison, and generalization. Findings. A comparison and analysis of publicly available information on AI-based cybersecurity technologies and tools was performed. As a result of the performed scientific analysis, it was established that AI tools play an important role in detecting and deterring threats of unauthorized intrusions into the security system of the military network. It has been found that AI is now a necessity for every computer system and network. The use of AI technologies in the form of intelligent agents is an effective tool for protecting against cyber attacks and assessing vulnerabilities and risks in cyberspace. During the 24/7 streaming analysis of large volumes of data in real-time, AI can identify patterns and provide recommendations for possible remediation of identified vulnerabilities. AI-powered cyber defense software responds to attacks by isolating affected systems. Originality. Areas of possible application of AI in military cyber operations were analyzed and systematized. Technologies of machine and deep learning, and artificial neural networks for identification and prediction of cyber threats are considered. Practical value. The obtained results of the performed analysis indicate the powerful potential of using intelligent technologies and tools for the military industry. AI technologies provide cybersecurity teams with flexibility, scalability, and the ability to automate threat detection. The implementation of AI in the cyber security of the military sphere can significantly reduce cyber risks, reduce costs, and optimize the processes of detection, research, response, and monitoring of cyber threats in real time. The paper contributes to the intensification of proactive detection and analysis of potential cyber threats in conjunction with incident and threat response activities in the fight against cybercrime. Ultimately, these advances will help our cyber military to be better prepared and equipped for the challenges and risks of modern warfare. [ABSTRACT FROM AUTHOR]

Autonomous navigation is currently subject to particular interest for naval and commercial vessels. In addition to the regulatory effort, several pieces of research tackled the development of new guidance laws, stable and robust control algorithms, methodologies to increase situational awareness, and collision avoidance algorithms. However, most of these systems blindly trust information from navigation sensors, which can malfunction or, even worse, have their data altered by cyber-attacks. This last scenario will be increasingly common shortly and represents a dangerous threat that future highly computerised ships must face. The proposed work shows an approach capable of hijacking a state-of-the-art collision avoidance algorithm route by feeding it with rogue data. The effects of such a cyber-attack are shown via an extensive simulation campaign, testing the methodology on three different COLREG scenarios: head-on, crossing, and overtaking. The study additionally highlights the susceptibility of forthcoming navigation systems to security breaches, which still needs to be addressed in the existing literature. According to the experimental findings, attackers can attract the autonomous ship guidance toward their objective at a mean distance of $ 133\,{\rm m} $ 133 m . Finally, suggestions on how to harden these systems against this family of attacks are provided in the conclusion. [ABSTRACT FROM AUTHOR]

Summary Recently, the United States has continuously exposed big confidentiality cases, involving individuals ranging from former or current presidents to retired soldiers, which on the one hand shows that the protection of state secrets is facing the challenges of the digital age, on the other hand, it also reveals the full coverage of the US confidentiality legal system. This paper argues that, as a pioneer in information technology and a cyber power in digital technology, the self-reflection and innovation of the US confidentiality legal system in keeping with the times reflect the development trend of future confidentiality legal systems, which is worth learning from when revising and improving confidentiality laws in China. The experience of the United States rule of law in confidentiality indicates that under the rapid development of digital technology, the national confidential legal system needs to establish a concept of classified information infrastructure that conforms to the characteristics of the times, and fully consider the security of endpoints. system environments personnel and corresponding capabilities for establishing, maintaining managing and destroying classified information, so as to actively respond to environmental changes and challenges caused by the application of network information technology. After World War II, the United States modernized and reformed its classified information infrastructure through laws and regulations constantly adjusting the delicate balance between information disclosure and confidentiality. Under the guidance of a series of executive orders of President Roosevelt. the classified information infrastructure was changed from wartime to peacetime. After the 9/11 incident, President Obama's Executive Order No. 13526 further transformed the confidentiality infrastructure, established a comprehensive controlled unclassified information system, and gradually formed a set of classified information security system construction. Different from other legal systems in the United States presidential executive orders have become an important component and main source of the US confidentiality legal system due to their relatively simple and efficient legislative process. After legislation, the US government attaches great importance to the effective implementation of laws, that is, supporting regulations, requiring relevant departments to timely formulate or modify standards, manuals and other normative documents for implementation. These standards and manuals are important tools for ensuring the effective implementation of confidentiality infrastructure laws, and also demonstrate the strong technical characteristics of their confidentiality infrastructure legal system. This paper proposes that for a long time the legislation on confidential infrastructure in the United States has been guided by national security strategies, and focuses on supporting cooperation of related industries and enterprises. In cyber society. the relevant laws of the United States have also strengthened the connection between confidential infrastructure and cyber security: in the face of the new international security situation brought about by the Russia- Ukraine conflict, the flexibility of its confidentiality law also supports the transfer of security benefits under its strategic intention. The current status and trends of legislation on confidentiality infrastructure in the United States. as well as the flexible support of legal enforcement for strategy, have provided new ideas for the construction and improvement of China's confidentiality legal system from the legislative concept of confidentiality law. the modernization of confidentiality rule of law and the standardized content and form of confidentiality legal system. [ABSTRACT FROM AUTHOR]

Copyright of Romanian Journal of Intellectual Property Law / Revista Română de Dreptul Proprietăţii Intelectuale is the property of Universul Juridic Publishing House 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.)

This study investigates high school students' perceptions of cybersecurity careers, focusing on two primary research questions: (1) What are the perceptions of high school students about a career in cybersecurity? and (2) What level of exposure to cyber careers do high school students have? The study used a 21-item survey instrument and collected 208 responses from high school students. The results suggest that students' preparedness and confidence in pursuing cybersecurity careers are significantly influenced by three key factors: high awareness of cybersecurity issues, early exposure to cyber-related activities, and access to dedicated cybersecurity courses. The findings indicate that students who are more aware of cybersecurity concepts and challenges, often through social media, extracurricular involvement, or personal interest, exhibit a stronger interest and confidence in cyber careers. Any other exposure to cyber security careers through cyber clubs, competitions, and hands-on workshops enhances perceived readiness. Additionally, participation in specialized cybersecurity courses within the school curriculum is associated with increased feelings of preparedness and capability to enter the cybersecurity workforce. Our study emphasizes raising awareness, providing early practical experiences, and integrating comprehensive cybersecurity education in high schools to better prepare students for careers in this critical and growing field. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of InformaticsTechnologies is the property of Institute of Informatics, Gazi 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.)

Among the most well-known machine learning algorithms, Extreme Learning Machine (ELM) has seen widespread use across a variety of fields, including the defence and military industries. For problems like sluggish technique and iteratively altering the hidden layer's parameters to optimise the efficiency of the gradient descent approach, a cutting-edge machine learning algorithm known as the ELM has been developed. Depending on the specific objective and circumstance, the Extreme Learning Machine (ELM) may be more appropriate than Deep Neural Network (DNN) techniques. The models constructed in this manner perform quite well in generalisation. The following three goals are emphasised in its unconventional structure: 1) A great degree of accuracy in learning 2) less need for direct human involvement 3) an extremely rapid rate of learning, and moreover, it provides an optimal response for the whole world. As a result of its quick training, flexibility, and resilience, the Extreme Learning Machine (ELM) has several uses in this field, including target detection and tracking, image and signal processing, cybersecurity and intrusion detection, decision support systems, pattern recognition and classification, etc. According to our findings, the ELM approach was used with low training time and the testing accuracy is excellent. Also, this study presents the contribution of the revolutionary machine learning algorithm ELM to the defence and military sectors. [ABSTRACT FROM AUTHOR]