Cyber Threats Research Articles

New Heuristics Method for Malicious URLs Detection Using Machine Learning

Malicious URLs are a very prominent, dangerous form of cyber threats in view of the fact that they can enable many evils like phishing attacks, malware distribution, and several other kinds of cyber fraud. The techniques of detection conventionally applied are based on blacklisting and heuristic analyses, which are gradually becoming inefficient against sophisticated, rapidly evolving threats. In this paper, the authors present various machine learning techniques applied in malicious URL detection. In the present paper, we will look at three machine learning models: Logistic Regression, Random Forest, and Support Vector Machines. We used a methodology that involved collecting data and feature extraction, training a model, then evaluating its performance with different metrics such as accuracy, precision, recall, and F1-score. We implemented and optimized three models—Logistic Regression, Random Forest, and Support Vector Machines (SVM)—based on the literature available that indicates the effectiveness of these models. Logistic Regression shows promising results to detect the malicious URLs, according to Vanitha and Vinodhini. Random Forest models are found to be very robust and accurate according to Cui et al. and Vanhoenshoven et al., SVM models are evidenced to have very high accuracy according to Manjeri et al., Further works on deep learning models emphasized their potentials. In our study, the optimized Random Forest model in our case showed the best performance, and its training accuracy was 99%, while validation accuracy was 90.5%, also logistic Regression and SVM achieved training accuracy was 89.31%, while validation accuracy was 90.5%. All the optimization processes, model performances, and integration into the real-time cybersecurity infrastructures, along with the strengths and limitations, are discussed in this paper. The paper will, therefore, discuss the benefits and challenges for each model in this aspect—emphasizing continuous updating of the models and integrating them into real-time cybersecurity infrastructures.

A Study on Designing Cyber Training and Cyber Range to Effectively Respond to Cyber Threats

As cyberattacks become increasingly sophisticated with advancements in information and communication technology, the impact of cyberspace threats is growing in both civilian and defense sectors. The utilization of cyber capabilities in operations is on the rise, prompting major nations to continuously enhance their cyber capabilities. This study aims to establish a systematic approach to cyber operations training and propose a framework for the development of cyber training. A hybrid cyber training system is designed as a plan for temporal and spatial integration to simultaneously combine simulation-based training with real-world target training. To develop this concept, a literature review was conducted, expert consultations were held, and data were collected and analyzed through visits to relevant organizations and units. Additionally, the fundamental components of cyber training were examined from environmental, scenario-based, and operational perspectives, leading to the presentation of a development direction for effective cyber training. This study is anticipated to enhance response capabilities to evolving cyber threats and attacks, improve cyber operational proficiency, and secure cyber power to achieve dominance in cyberspace.

Evolving Adversarial Training (EAT) for AI-Powered Intrusion Detection Systems (IDS)

Intrusion Detection Systems (IDS) are crucial components of network security, yet traditional IDS models often fail to cope with rapidly evolving adversarial attacks that exploit their static nature. This study proposes a novel approach, Evolving Adversarial Training (EAT), to enhance the adaptability and robustness of AI-powered IDS against dynamic threats. The EAT framework integrates continuous model evolution with advanced adversarial training techniques, enabling the IDS to dynamically adjust to new attack patterns. Experimental results demonstrate that the EAT framework significantly enhances IDS performance, leading to increased detection accuracy and reduced false positive rates compared to conventional methods. These findings emphasize the potential of EAT in fortifying network defenses against evolving cyber threats, offering a promising avenue for future research in scalable and adaptive IDS solutions that can effectively combat the complexities of modern cyber adversaries. The research explores three key objectives: dynamic adaptation and adversarial training, continuous learning and enhanced threat detection, and robustness and generalization. By focusing on these objectives, the study aims to develop AI-powered IDS that can effectively navigate the ever-changing cyber threat landscape. The research methodology includes data collection, model architecture design, training and evaluation, continuous learning, simulation, and real-world testing, all aimed at enhancing the resilience of AI-powered IDS against adversarial attacks. By systematically following this framework, the study intends to enhance the security system of IDS through the effective implementation of EAT.

A privacy-preserving Self-Supervised Learning-based intrusion detection system for 5G-V2X networks

In light of the ongoing transformation in the automotive industry, driven by the adoption of 5G and the proliferation of connected vehicles, network security has emerged as a critical concern. This is particularly true for the implementation of cutting-edge 5G services such as Network Slicing (NS), Software Defined Networking (SDN), and Multi-access Edge Computing (MEC). As these advanced services become more prevalent, they introduce new vulnerabilities that can be exploited by cyber attackers. Consequently, Network Intrusion Detection Systems (NIDSs) are pivotal in safeguarding vehicular networks against cyber threats. Still, their efficacy hinges on extensive data, which often contains sensitive and confidential information such as vehicle positions and owner’s behaviors, raising privacy concerns. To address this issue, we propose a Privacy-Preserving Self-Supervised Learning (SSL) based Intrusion Detection System for 5G-V2X networks. The majority of works in the literature relying on Federated Learning (FL) and often overlook data labeling on the end devices. Our methodology leverages SSL to pre-train NIDSs using unlabeled data. Post-training is then performed with a minimal amount of labeled data, which can be carefully crafted by an expert. This novel technique allows the training of NIDSs with huge datasets without compromising privacy, consequently enhancing the efficacy of cyber-attack protection. Our innovative SSL pre-training methodology has yielded remarkable results, demonstrating a substantial improvement of up to 9% in accuracy across a diverse range of training dataset sizes, including scenarios with as few as 200 data samples. Our approach highlights the potential to enhance automotive network security significantly, showcasing groundbreaking achievements that set a new standard in the field of automotive cybersecurity.

Reducing the risk of social engineering attacks using SOAR measures in a real world environment: A case study

The global cost of successful cyberattacks is increasing annually, with there being a shift towards social engineering threats in recent years. Cybercriminals are increasingly targeting humans rather than technical systems, recognizing data as a critical resource, especially in the finance industry where breaches can lead to substantial losses and reputational damage. The present case study proposes measures to reduce human susceptibility to social engineering attacks, leveraging SOAR (Security Automation, Orchestration, and Response) technology for incident response automation. The study covers various issues in cybersecurity, SOAR, and social engineering, through analyzing interviews with expert practitioners in the field, addressing cybersecurity skills shortages and current cyber threats. Four social engineering vignettes were developed, representing real threats, along with specific SOAR measures implemented using Microsoft Sentinel. These measures were simulated to demonstrate their effectiveness by reducing the employee's vulnerability to social engineering attacks. The risk of social engineering attacks was successfully reduced by implementing a responsive approach through the developed SOAR measures. Some of the measures reduced the risk by locking user accounts or forcing password changes after a detected cyber incident while another measure was developed for awareness enhancements. Given the current shortage of cybersecurity professionals, technologies like SOAR are becoming increasingly relevant for security teams. However, SOAR alone cannot address all challenges posed by social engineering and should be viewed as a complementary measure rather than a standalone solution.

Analysis of Financial Performances for CHKP: Comparison with QLYS, RPD and GEN

Due to the escalating cyber threats, the cybersecurity industry has experienced significant growth, necessitating a detailed financial performance study of market leaders to provide a comprehensive picture of company health and market position. This study investigates the financial performance of four leading cybersecurity companies: Check Point Software Technologies Ltd. (CHKP), Qualys Inc. (QLYS), Rapid7 Inc. (RPD), and Gen Digital Inc. (GEN) over the period from 2019 to 2024. The analysis reveals that Check Point consistently maintains high margins and stable profitability, making it the best investment among the four. In contrast, Qualys, Rapid 7, and Gen Digital show less competitive performance due to various operational and profitability challenges. The study acknowledges its limitations, including reliance on quantitative metrics and a limited timeframe, but underscores the promising prospects for the cybersecurity industry driven by the increasing need for advanced digital security solutions. These results provide valuable insights for investors and stakeholders in making informed decisions in the dynamic cybersecurity sector.

An expert system of recommendations for combating cyber threats using CVSS metrics and game theory

This study is focused on the creation of an expert system for generating recommendations on cyber security. The developed expert system uses a game-theoretic model as a inference engine to transform expert knowledge into recommendations for end-users, who may be chief IT security officers (CISOs), system administrators, or cyber security engineers. Expert knowledge is presented in the form of an estimate of the base group of CVSS metrics - Common Vulnerability Score System, for each type of attack and adjusted values of CVSS in the case that the counterattack strategy is applied. Given a set of attacks and a base of expert attack knowledge, the system generates a game matrix of zero-sum game with a cybercriminal and a cyberdefense expert as players. The inference engine of the expert system is a game-theoretic model responsible for solving the game using the Brown-Robinson iterative method and generating cyber protection recommendations. An experiment was conducted on the convergence of the BrownRobinson algorithm on the 2022 vulnerability dataset from the Cybersecurity and Infrastructure Security Agency database, as a result of which it was determined that the convergence of the algorithm for solving the matrix game is achieved at a number of iterations of 1000. As a result of the work, expert system was designed and implemented along with the Web interface, which provides input by experts of CVSS level assessments of collected threats, threats countermeasures and output of recommendations for combating cyber threats

Распознавание фишинговых ссылок с использованием методов машинного обучения

In recent years, phishing has become one of the most widespread and dangerous cyber threats. These attacks aim to obtain users' confidential information, such as passwords and credit card details, through deceptive messages or websites, making the issue of protection against them more relevant than ever. Traditional methods of phishing protection, such as blacklists and heuristic analysis, can no longer keep up with the evolving pace of phishing attacks. Therefore, there is a need to develop more advanced and intelligent methods, among which machine learning (ML) techniques play a significant role. This article discusses various ML methods used for automatic detection of phishing URLs. The study presents the main approaches, model architectures, advantages and disadvantages of each method, and provides a comparative analysis of their effectiveness on real data.

ASAP: Automatic Synthesis of Attack Prototypes, an online-learning, end-to-end approach

Zero-day attack detection and categorization is an open-research field where four main context factors need to be taken into account: novel or zero-day attacks (i) are unlabeled by definition, (ii) may correspond to out-of-distribution data, (iii) can arise concurrently, and (iv) distribution shifts in the feature space need online-learning. Given such constraints, the online detection and categorization of new cyber threats can be modeled as a heterogeneous collective anomaly detection problem, for which no online-learning solutions exist purely based on back-propagation. To this respect, this paper presents an online-learning, end-to-end back-propagation strategy for Automatically Synthesizing the potential signatures or Attack Prototypes of novel cyber threats (asap). The presented framework incorporates automatic feature engineering, operating over raw data from the OpenFlow monitoring API and raw bytes of traffic captures. In asap, specialized inductive biases enhance the training data efficiency and accommodate the inference machinery to resource-constrained scenarios such as the Internet of Things. Finally, the validity of this framework is demonstrated in a live training experiment comprising IoT traffic emulation 33To foster research in the field, the source-code of asap alongside instructions to reproduce the experiments are made publicly available among the set of SmartVille NID models in [1]..

КОНВЕРГЕНЦІЯ МАРКЕТИНГУ ТА ФІНАНСОВО-ЕКОНОМІЧНОЇ БЕЗПЕКИ В УМОВАХ ЦИФРОВОЇ ТРАНСФОРМАЦІЇ

The article examines the relationship between marketing and financial and economic security in the context of the development of information technologies, as their convergence is an integral part of modern business. Digital transformations significantly change the landscape of marketing and financial and economic security, in order to work successfully in new conditions, enterprises must invest in digital technologies, protect customer data and systems from cyber attacks and fraudulent actions, constantly adapt to new technologies and market conditions. In today's conditions, economy, rationality and maximum efficiency based on the use of modern information and multimedia technologies are a guarantee not only of quality management, but also of the company's overall competitiveness in the markets. Marketing plays a key role in ensuring the financial and economic security of the enterprise, i.e. constant monitoring of the market, analysis of competitors and study of customer needs allow timely detection of potential threats and development of effective strategies to neutralize them. Thanks to a deep understanding of customer needs and market dynamics, marketing contributes to the development of effective strategies that allow the enterprise to adapt to changes and maintain its competitiveness, optimize its costs, improve product quality and strengthen its position in the market. In the conditions of growing global competition, companies must understand the development trends of international markets, cultural characteristics and take into account legislative norms in their activities. In this case, marketing helps to adapt business to new conditions, and economic security provides protection from external threats. The rapid development of digital technologies is changing the rules of the game in the market, while marketing allows effective use of digital channels to interact with customers, and economic security provides protection against cyber threats and other risks related to digital technologies. Consumers increasingly expect a personalized approach and high quality products and services, so marketing helps to understand the needs of customers and develop appropriate offers, and financial and economic security ensures that these offers will be fulfilled with quality and time. New players are constantly entering the market, which complicates competition, so there is a need to stand out among competitors, determine the company's competitive advantages, build a positive image and effectively manage crisis situations. In this case, economic security protects against unfair competition, ensures business stability in conditions of uncertainty, helps protect company data from cyber attacks and fraudulent actions, and prevents financial losses. Therefore, ensuring financial and economic security during marketing operations is a complex task that requires a complex approach. The convergence of marketing and financial and economic security allows you to minimize risks, ensure sustainable business development and effective promotion of goods and services, and protect the reputation of the company and its assets. The interaction of marketing and economic security helps prevent crisis situations related to security breaches that can negatively affect the company's image, as well as avoid ineffective spending on marketing campaigns related to unsafe or unethical practices

Securing Automated Intelligence: Challenges and Solutions in RPA and Generative AI Integration

Integrating Robotic Process Automation (RPA) and Generative AI can revolutionize business processes, enabling greater efficiency, scalability, and intelligent decision-making. However, this powerful combination also introduces various security challenges and impacts to governance that organizations must address to protect sensitive data and maintain trust in automation systems. As RPA bots increasingly interact with AI models, vulnerabilities such as unauthorized data access, malicious model manipulation, and improper handling of sensitive information become more pronounced. These risks can lead to cyberattacks, data breaches, and regulatory compliance violations. This paper examines the security challenges inherent in RPA and Generative AI integration, focusing on three key areas: data privacy, model integrity, and automation governance. We assess how improper configurations and lack of security oversight can expose these systems to exploitation. Furthermore, we explore solutions such as implementing robust encryption protocols, secure data access controls, and continuous monitoring of AI model behavior to detect anomalies. By presenting case studies and evaluating emerging best practices, we offer a framework for safeguarding RPA and AI systems, ensuring that automation remains a trusted and secure tool for organizations. The paper also discusses aligning security strategies with regulatory requirements and industry standards. This approach enables organizations to unlock the full potential of RPA and Generative AI while mitigating risks and protecting against evolving cyber threats.

IDENTIFYING CYBERTHREATS THROUGH SOCIAL MEDIA RESEARCH

The work provides an overview of the main methods of social network analysis used to identify cyber threats. The main types of threats in social networks are indicated and some protection methods for their prevention are described. Typical tasks of social network analysis aimed at detecting cyber threats are, for example, identifying online communities, identifying leaders and experts in communities, analyzing the stability of communities, clustering textual information, etc. With increasing digitization and active use of social networks as a means of communication, the importance of effective monitoring and data analysis to ensure cyber security is becoming more and more relevant. Complexities and challenges associated with processing large amounts of data in social networks are also analyzed, including privacy issues and ethical aspects of user data control. The study provides a holistic view of the use of social media as a tool to proactively identify and prevent cyberthreats, highlighting the importance of integrating analytical systems into the overall cybersecurity framework of organizations and individuals. The study provides a holistic view of the use of social media as a tool for proactive detection and prevention of cyber threats, highlighting the importance of integrating analytical systems into the overall cybersecurity posture of organizations and individuals, while emphasizing the need to improve machine learning and artificial intelligence techniques for deeper and more accurate analysis of user behavior and community dynamics, which can contribute to more effective prevention and neutralization of threats in the digital environment.

LEGAL FRAMEWORKS FOR REGULATING CYBERCRIME AND CYBER TERRORISM

Cybercrime and cyber terrorism pose significant challenges to global security and stability in the digital age. This paper explores the legal frameworks established to regulate these phenomena, analyzing their effectiveness and limitations. It examines the evolving nature of cyber threats, the complexities of defining cybercrime and cyber terrorism, and the role of international cooperation in addressing these issues. Drawing on case studies and legislative examples, this paper identifies key strategies for enhancing legal frameworks to combat cyber threats in the 21st century. Keywords: Cybercrime, Cyber Terrorism, Legal Frameworks, Legislation, Cybersecurity, International Cooperation.

Integrating AI with aviation cybersecurity: The strategic role of human resources<sup></sup>

The escalating complexity of cyber threats in aviation highlights the crucial need to integrate advanced technologies and strategic human resource management. This study delved into incorporating artificial intelligence (AI) in aviation cybersecurity, explicitly focusing on the strategic role of human resources (HR) in supporting these endeavours. Semi-structured interviews with eight industry experts from Turkey were analysed using NVivo to identify critical themes such as HR's involvement in cybersecurity, challenges in AI implementation, and the significance of cultural integration and interdepartmental collaboration. Sentiment analysis with Python's TextBlob indicated high positivity towards AI and strategic HR involvement. The findings underscored the necessity for continuous education, strategic recruitment, and resilient cybersecurity frameworks, emphasising AI's transformative impact on real-time threat detection and predictive analytics. The research offered valuable insights and recommendations for enhancing aviation cybersecurity, highlighting the essential role of HR and the advantages of AI integration.

Cybersecurity in Healthcare: Securing Patient Health Information (PHI), HIPPA compliance framework and the responsibilities of healthcare providers.

Healthcare industry is major target for cyberattacks, making the protection of public health information (PHI) and Personal Identifiable Information (PII) a prime issue. In this digital era, with health organizations shifting to electronic health records and telemedicine, they are facing a major cybersecurity attack such as ransomware, phishing, and data breaches. These attacks compromise patient privacy, pose serious risks to patient safety, and hinder healthcare operations. The Health Insurance Portability and Accountability Act (HIPAA) provides a comprehensive compliance framework to protect PHI, wherein health providers shall undertake administrative, physical and technical safeguards. Despite these regulations, many healthcare providers struggle with achieving and maintaining HIPAA compliance due to limited resources, outdated technologies, and the rapidly evolving nature of cyber threats. This paper explores the HIPAA compliance framework, examining the specific responsibilities of healthcare providers to secure PHI. Key measures taken include periodic analysis of risks, establishment of encryption and access control systems, and comprehensive employee training to minimize risks of cyber-attacks. The study highlights that there is a growing need for healthcare providers to adopt proactive, adaptive cybersecurity strategies to deal with emerging threats. By following HIPAA regulations and updating security practices continuously, healthcare providers can protect the PHI, ensure regulatory compliance and safeguarding patient trust. The findings emphasize the role of adhering to regulation and innovation both in managing cyber risks for the healthcare sector.

The Role of Internet of Battlefield Things in Modern Warfare: A Cybersecurity Perspective

The Internet of Battlefield Things (IoBT) is revolutionizing military operations by integrating interconnected devices and autonomous systems into combat environments, significantly enhancing decision-making, communication, and battlefield efficiency. However, this reliance on IoBT introduces substantial cybersecurity risks, exposing military networks to sophisticated cyber threats such as hacking, malware, and Distributed Denial of Service (DDoS) attacks. This paper explores the interplay between IoBT and the cyber domain, identifying the key challenges, vulnerabilities, and solutions necessary to secure these interconnected systems in the context of modern warfare. The study reviews the architectural components of IoBT, including sensors, drones, and communication networks, and examines their potential weaknesses in the face of cyberattacks. Additionally, it discusses common attack vectors such as Man-in-the-Middle (MitM) and zero-day exploits, which pose significant risks to military systems. To counter these threats, the paper evaluates various cybersecurity strategies, including the application of artificial intelligence (AI), machine learning (ML), blockchain technology, and robust encryption protocols designed to enhance the resilience and security of IoBT networks. Case studies of real-world IoBT deployments and cyber breaches further illustrate the practical implications of these challenges and defense mechanisms.

Cybersecurity and geopolitical dimensions of external information interventions in Ukraine: Analysis of current trends

Introduction: From the year 2019, the country has experienced a series of cyber incidents affecting its key areas of infrastructure such as energy, finance, communication, government, healthcare, and technology. Purpose: This work aims to define and explain the contemporary threats in the sphere of cyber security, as well as the geopolitical aspect of external interference in Ukraine’s information space for the years 2019-2024. Method: Sources used to gather data for this research were government and cybersecurity firm reports, academic articles, news articles from reliable media houses, and social media listening tools. Quantitative data collected by statistical tools analyzed the trend as well as the intensity of cyber threats. They also included specific case exhibits as well as interviews with the experts to get more information. Results: The study indicated that there will be improved and more frequent attacks and that these will be more complex from 2019 to 2024 in Ukraine. It caused moderate disruption to the key sectors but the disruptions were especially noticed in sectors such as energy, finance, communication, government, healthcare, and technology. Conflict events in Donbas, the COVID-19 outbreak, the enhancement of military tensions, and the start of the Russian invasion of Ukraine in February 2022 were associated with increases in the level of cyber efforts. Conclusion: The conclusion points out that Ukraine needs effective international cooperation, the highest level of technological protection, and profound geopolitical analysis to safeguard cyberspace and prevent conflicts in Eastern Europe

Cyber-biological convergence: a systematic review and future outlook.

The introduction of the capability to "program" a biological system is referred to as Engineered biology and can be compared to the introduction of the internet and the capability of programming a computer. Engineered biology is supported by a digital infrastructure that includes data, data storage, computer-dependent laboratory equipment, internet-connected communication networks, and supply chains. This connectivity is important. It can improve workflows and enhance productivity. At the same time and unlike computer programs, biological systems introduce unique threats as they can self-assemble, self-repair, and self-replicate. The aim of this paper is to systematically review the cyber implications of engineered biology. This includes cyber-bio opportunities and threats as engineered biology continues to integrate into cyberspace. We used a systematic search methodology to review the academic literature, and supplemented this with a review of opensource materials and "grey" literature that is not disseminated by academic publishers. A comprehensive search of articles published in or after 2017 until the 21st of October 2022 found 52 studies that focus on implications of engineered biology to cyberspace. The search was conducted using search engines that index over 60 databases-databases that specifically cover the information security, and biology literatures, as well as the wider set of academic disciplines. Across these 52 articles, we identified a total of 7 cyber opportunities including automated bio-foundries and 4 cyber threats such as Artificial Intelligence misuse and biological dataset targeting. We highlight the 4 main types of cyberbiosecurity solutions identified in the literature and we suggest a total of 9 policy recommendations that can be utilized by various entities, including governments, to ensure that cyberbiosecurity remains frontline in a growing bioeconomy.

Ethical Hacking and Vulnerability Testing: Navigating Legal and Moral Boundaries

In today's interconnected world, cybersecurity is of paramount importance, with organizations facing ever-evolving threats from malicious actors seeking to exploit vulnerabilities in their digital infrastructure. Ethical hacking and vulnerability testing have emerged as crucial tools in the fight against cyber threats, enabling organizations to proactively identify and address security weaknesses before they can be exploited by attackers. However, the practice of ethical hacking raises complex ethical and legal considerations, as practitioners navigate the delicate balance between security imperatives and individual rights. Keywords: Ethical Hacking, Vulnerability Testing, Cyber Law, Ethics, Cybersecurity Ethics, Legal Boundaries.

An Examination of Threats and Countermeasures Relating to Healthcare Cyber Risks: The Case of Kenyatta National Hospital

Background Africa has seen an exponential increase in internet penetration and ICT affordances since the turn of the twenty-first century. Healthcare institutions are scrambling to put in place the appropriate safeguards to protect their patients' data from unauthorized access since the need to protect private information has become critical, particularly for cybercriminals eyeing the data of medical patients. This thesis investigates cyber security threats and countermeasures in healthcare, with a focus on Kenyatta National Hospital (KNH). Given Africa's increased internet use and the critical need to protect patient data from cybercriminals, the study explores how data protection and cyber security influence healthcare delivery at the hospital.  Key Objectives To examine cyber threats and countermeasures employed by KNH as well as analyzing the impact of Kenya's Cybercrime Act.  Results The survey at Kenyatta National Hospital shows strong cybersecurity measures, with 89% having dedicated resources and 88% using computers regularly. Regarding the Kenya Cybercrime Act, 74% know how to detect and report hacks, though 8% have encountered malware and 12% lack basic malware knowledge. 78% have anti-virus software, and 63% verify email attachments, while knowledge of social engineering and email scams is limited, revealing a need for further education. The second objective looked at the impact of Kenya Cybercrime Act, as a local data protection laws on supporting patient- healthcare system at Kenyatta National Hospital. A significant majority, 74%, are aware of when their computer is hacked or infected and know whom to contact in such cases. The results also show that 79% of respondents have never encountered a virus or trojan on their computers. When opening email attachments, 63% of respondents always verify that the attachment is from a known and expected source. Knowledge of social engineering attacks is limited, with only 18% of respondents aware of these threats and 82% unfamiliar with them. Regarding email scams, 51% do not know what an email scam is or how to recognize one, underscoring a need for further training. Finally, while 85% of respondents believe their computers are not valuable to hackers, 15% recognize their potential as targets, reflecting differing perceptions of risk and emphasizing the need for ongoing cyber security education.