Cybersecurity Threats Research Articles

Strengthening Compliance with Data Privacy Regulations in U.S. Healthcare Cybersecurity

This study evaluates the state of data privacy and cybersecurity compliance within the U.S. healthcare sector, leveraging data from the U.S. Department of Health & Human Services Breach Portal, Verizon Data Breach Investigations Report, and the Health IT Dashboard. A quantitative methodology comprising descriptive statistical analysis, K-means clustering, and multivariate regression was employed to examine healthcare data breaches, categorize cybersecurity threats, and identify compliance challenges. Findings revealed a persistent increase in breaches, with hacking/IT incidents comprising over 80% of breaches in 2020 and a peak of 135 incidents in 2021. Budget allocation emerged as the most significant predictor of compliance (p = 0.0178), affirming resource constraints. Malware and ransomware were identified as dominant threats, while insider threats emerged as high-impact vulnerabilities. The study recommends increasing cybersecurity budgets, implementing continuous staff training, harmonizing regulations, and adopting Cybersecurity Maturity Models to systematically enhance security postures. The study provides critical insights into the challenges faced by healthcare organizations in achieving compliance with evolving data privacy regulations such as HIPAA and HITECH. The findings highlight the economic and operational implications of non-compliance, including financial penalties, reputational harm, and patient trust erosion. The study further affirms the importance of strategic investments in advanced cybersecurity tools, policy harmonization, and employee education. Hence, policymakers and healthcare administrators can utilize these insights to foster a robust culture of compliance, ensuring the protection of sensitive patient information and the resilience of healthcare operations against cyber threats. The study suggests that future research explores integrating artificial intelligence, zero-trust architectures, and adaptive risk management frameworks to further enhance cybersecurity strategies and regulatory compliance.

Insider Threats: The Cybersecurity Analysis using OCTAVE Allegro which are combined with HAIS-Q

There are various types of cybersecurity threat in the globe, one of which is an insider threat. Because the current vulnerability generates an insider threat, SMEs suffer. In this situation, the company suffers a loss of profit and trust. Because of the speed and intensity of cybersecurity, particularly internal threats, SMEs must conduct regular vulnerability assessments. Insider threats to cyber security are a major issue in today's environment. Insider threats come in a variety of flavours, one of which is the unintended insider threat, or UIsT. This type of threat is a real one, and it is important to understand who and how they can become an insider threat.

Evaluating and Mitigating Cybersecurity Threats from System Update Vulnerabilities through the CrowdStrike Case

Evaluating and Mitigating Cybersecurity Threats from System Update Vulnerabilities through the CrowdStrike Case

Exploring Digital-Only Banking in Malaysia: Technological Advancements, Regulatory Landscape, and Business Implications

Digital banking in Malaysia is undergoing rapid transformation, driven by technological advancements, regulatory frameworks, and evolving consumer preferences. This shift is marked by the adoption of critical technologies such as Application Programming Interfaces (APIs), low-code technology, cloud computing and artificial intelligence (AI) which are redefining how financial services are delivered. These innovations enhance operational efficiency, enable seamless integrations, and offer personalized customer experiences, positioning digital banking as a cornerstone of Malaysia’s financial ecosystem. Concurrently, regulatory measures introduced by Bank Negara Malaysia (BNM), including digital banking licenses and consumer protection policies, have facilitated a conducive environment for growth while ensuring financial inclusion and stability. Despite these advancements, the transition to digital-only banking is not without challenges. Cybersecurity threats, compliance complexities and technological dependency pose significant risks to both financial institutions and consumers. Addressing these issues requires a robust cybersecurity framework, continuous innovation and collaborative efforts among policymakers, industry players and technology developers. This paper reviews critical technologies shaping the industry, explores the regulatory landscape, evaluates security concerns, and examines the business implications and opportunities. The findings provide insights into the future of digital banking in Malaysia, focusing on innovations, challenges and potential growth areas.

The Integration of Digital Currencies with Traditional Financial Systems

Digital currencies like Bitcoin and Ethereum are reshaping the financial landscape by challenging traditional banking and commerce. These technologies hold significant potential to enhance financial inclusion by providing access to underserved populations and streamlining cross-border payments, making transactions faster and more cost-effective. They also empower decentralized finance (DeFi), enabling innovative financial services without intermediaries. Blockchain, the foundation of digital currencies, facilitates secure, decentralized, and programmable systems, supporting key innovations like asset tokenization and Central Bank Digital Currencies (CBDCs). These advancements bridge the gap between digital and traditional financial systems, promoting efficiency and accessibility. Despite their promise, digital currencies face challenges, including cybersecurity threats, market volatility, and fragmented regulatory frameworks. Case studies on CBDCs and private-sector blockchain initiatives demonstrate viable integration pathways and underline the need for robust governance. By implementing balanced policies, stakeholders can harness the benefits of digital currencies while mitigating associated risks. These measures will be crucial to fostering a stable, inclusive, and innovative financial ecosystem that addresses global economic disparities and drives sustainable growth.

ЦИФРОВІЗАЦІЯ І ПІДПРИЄМНИЦТВО: ОСНОВНІ АСПЕКТИ СЬОГОДЕННЯ

The modern world is rapidly changing under the influence of digital technologies. All spheres of life, including entrepreneurship, have undergone significant transformations. Until recently, traditional business relied on traditional, fundamental methods of management, marketing and communications, today in the conditions of sustainable development, entrepreneurs face the challenges of the digital age and have to adapt to new realities. Digitalization has become one of the main drivers of economic development, in particular in Ukraine. It allows you to optimize business processes, open new markets, improve the quality of customer service and significantly increase the competitiveness of enterprises. However, along with new opportunities, digitalization also creates certain challenges that require rapid adaptation and implementation of new strategies. Purpose of the study.The purpose of the study is to determine the main aspects of the interaction of digitalization and entrepreneurship by identifying the advantages, challenges and prospects for the development of such a combination. The object of the study is the process of interaction of digitalization and entrepreneurship. The methods used in the study were modern methods and principles of scientific knowledge, general theoretical and specific research methods, which allowed us to reveal the process of digitalization in entrepreneurship. The main hypothesis of the study was the assumption of the possibility of improving traditional entrepreneurship by introducing digitalization aspects. Presentation of the main material of the study. The work pays attention to the process of digitalization and entrepreneurship. By revealing the main aspects, the authors identified key positive aspects, which consist in automation, big data analysis, the use of artificial intelligence, increasing labor productivity, etc. At the same time, the authors of the study emphasize the need to disclose the risks associated with the digitalization process in entrepreneurial activity, namely: cybersecurity threats, regulatory restrictions, resistance to change, etc. The authors confidently noted the stability of modern tendencies and trends, which are clearly visible and affect business development. The originality and practical significance of the study is confirmed by the possibility of using certain conclusions at enterprises that are currently gradually implementing digitalization. Conclusions and prospects for further research. Digitalization has become an integral part of the development of modern entrepreneurship. It opens up new opportunities for growth, increasing business efficiency and competitiveness. However, to achieve success in the digital economy, entrepreneurs need to be flexible, ready for change and constantly adapt their strategies to new market conditions. A comprehensive view of digital transformation shows that the megatrend of digitalization not only brings with it progressive technological innovations, but also poses new challenges for companies. To survive in the competition for digital excellence, it is necessary to develop a digital strategy for the future that takes into account not only current events, but also long-term prospects. Sustainable development of digitalization requires flexible adaptation of corporate culture and structures to the ever-changing digital landscape.

The Top Five Cybersecurity Threats—The Future Is Now

The Top Five Cybersecurity Threats—The Future Is Now

Enhancing Cybersecurity Attack Detection Using Multiplayer Battle Game Optimizer with Hybridization of Deep Learning Models

Cybersecurity is advancing and the rate of cybercrime, which is always rising. Advanced attacks are measured as the novel normal as they are one of the more normal and extensive. Cybersecurity threats have risen promptly in many areas like healthcare, smart homes, energy, automation, agriculture, and industrial processes. An intrusion detection system (IDS) discovers intrusions by analyzing attack designs or mining signatures from system packets. To assess an IDS model, use Machine Learning (ML) and deep learning (DL) approaches for recognizing data traffic into malicious and healthy. ML and DL techniques has earned an extensive interest on countless applications and domains of study, mostly in Cybersecurity. With computing power and hardware becoming more available, ML and DL systems can be employed in order to classify and analyze corrupt actors from a massive group of accessible data. This manuscript presents an Enhancing Detection of Cybersecurity Attack Using Multiplayer Battle Game Optimizer with Hybrid Deep Learning (EDCA-MBGOHDL) technique. The main intention of the EDCA-MBGOHDL technique is to provide a robust framework for cyberattack detection using deep learning integrated with a hyperparameter tuning approach. At first, the feature selection process is implemented by applying improved Harris hawk optimization (IHHO) algorithm for ensuring that only the most relevant features are fed into the model. Furthermore, the hybrid of convolutional neural network, bidirectional long short-term memory and attention mechanism (CNN-BiLSTM-AM) model is employed for the classification of cybersecurity threats. Eventually, the multiplayer battle game optimizer (MBGO) algorithm adjusts the hyperparameter values of the CNN-BiLSTM-AM classifier optimally and outcomes in greater classification performance. The wide range of analysis of the EDCA-MBGOHDL technique takes place using a benchmark dataset. The outcomes pointed out the superior performance of the EDCA-MBGOHDL system across existing models

DLIIoT: A Deep Learning based Intelligent Attack Detection in IoT Networks using Cooja Simulator

Internet of things (IoT) has incredibly transformed the whole domain of communication process. The extensive dependency on these devices leads to various advanced cyber security threats. IoT devices fall easily into the ambit of malicious threats and are susceptible to vast range of attacks due to their limited computation capabilities and memory constraints. Intrusion Detection Systems (IDSs) are dedicated outstanding frameworks to protect these devices from cyber threats. In this study, a comprehensive review of different AI based IDS applied on IoTs is done. It has been observed that machine learning and deep learning has widely influenced the domain of IoT security. The focus of the research carried out is to earmark the techniques that are performing best on a given data set. Features selection, type of attacks, proposed solutions in solving security menaces are taken into consideration. Further, we have presented DLIIoT, a deep learning based intelligent attack detection in IoT networks by generating precise IoT datasets in Cooja Simulator. Four Deep learning algorithms are utilized and analysed under standard performance criteria metrics such as Precision, Recall, Accuracy and F1-score. It was found that deep learning algorithms have remarkable potential in detecting and recognizing malicious data patterns in IoT networks.

Harnessing machine learning for dynamic defence in the battle against 5G cybersecurity threats

Harnessing machine learning for dynamic defence in the battle against 5G cybersecurity threats

BRICS in the Digital Age: Investigating Technological Innovation and Knowledge Sharing as Catalysts for Economic Transformation

The digital age presents unparalleled opportunities for economic transformation, particularly for emerging economies like BRICS (Brazil, Russia, India, China, and South Africa). This study investigates the role of technological innovation and knowledge sharing as catalysts for economic development through a normative juridical analysis of legal frameworks. The findings reveal significant disparities in intellectual property laws, data governance policies, and innovation ecosystems among BRICS nations, hindering seamless collaboration. The study emphasizes the need for harmonized juridical mechanisms to address challenges such as digital inequality, cybersecurity threats, and geopolitical tensions. By proposing unified intellectual property agreements, standardized data governance policies, and joint R&D programs, the research offers actionable insights into fostering innovation and collaboration for sustainable economic transformation in the digital era.

GenSQLi: A Generative Artificial Intelligence Framework for Automatically Securing Web Application Firewalls Against Structured Query Language Injection Attacks

The widespread adoption of web services has heightened exposure to cybersecurity threats, particularly SQL injection (SQLi) attacks that target the database layers of web applications. Traditional Web Application Firewalls (WAFs) often fail to keep pace with evolving attack techniques, necessitating adaptive defense mechanisms. This paper introduces a novel generative AI framework designed to enhance SQLi mitigation by leveraging Large Language Models (LLMs). The framework achieves two primary objectives: (1) generating diverse and validated SQLi payloads using in-context learning, thereby minimizing hallucinations, and (2) automating defense mechanisms by testing these payloads against a vulnerable web application secured by a WAF, classifying bypassing attacks, and constructing effective WAF security rules through generative AI techniques. Experimental results using the GPT-4o LLM demonstrate the framework’s efficacy: 514 new SQLi payloads were generated, 92.5% of which were validated against a MySQL database and 89% of which successfully bypassed the ModSecurity WAF equipped with the latest OWASP Core Rule Set. By applying our automated rule-generation methodology, 99% of previously successful attacks were effectively blocked with only 23 new security rules. In contrast, Google Gemini-Pro achieved a lower bypass rate of 56.6%, underscoring performance variability across LLMs. Future work could extend the proposed framework to autonomously defend against other web attacks, including Cross-Site Scripting (XSS), session hijacking, and specific Distributed Denial-of-Service (DDoS) attacks.

Advancing national security and economic prosperity through resilient and technology-driven supply chains

In today’s globally interconnected world, resilient and technology-driven supply chains are pivotal to ensuring national security, economic stability, and competitive growth. The complexities of modern trade, coupled with geopolitical tensions, natural disasters, and cybersecurity threats, have exposed vulnerabilities in traditional supply chain systems. These disruptions not only threaten critical infrastructure but also undermine economic prosperity by increasing costs and reducing efficiency. Advanced technologies such as blockchain, artificial intelligence (AI), and the Internet of Things (IoT) offer transformative solutions to these challenges. Blockchain enhances transparency and security by providing immutable records of transactions, ensuring accountability across supply chain networks. AI optimizes operations through predictive analytics, improving demand forecasting, inventory management, and risk assessment. Meanwhile, IoT enables real-time monitoring, allowing stakeholders to track assets, environmental conditions, and logistics with unprecedented precision. Together, these technologies empower supply chains to adapt dynamically, fostering resilience against disruptions. Beyond operational improvements, technology-driven supply chains hold strategic importance for national security. Secure and transparent supply chains mitigate risks associated with critical resources, such as semiconductors and energy, while fostering domestic manufacturing capabilities. Economically, these innovations enhance efficiency, create jobs, and enable businesses to remain globally competitive. This paper explores the intersection of technology and supply chain resilience, highlighting their contributions to national security, economic prosperity, and sustainable development. It emphasizes the need for robust policy frameworks, interdisciplinary collaboration, and scalable solutions to ensure the future of secure and efficient global trade systems.

Enhancing Digital Resilience: A Cybersecurity Readiness Assessment of Kenyan TVET Institutions

Technical and Vocational Education and Training (TVET) institutions play a critical role in preparing the Kenyan workforce for the demands of the digital age. The rapid digitalization and widespread adoption of technology in these institutions poses significant cybersecurity threats, potentially compromising sensitive data, disrupting academic activities, and undermining the overall integrity of these institutions. This study aimed to assess the cybersecurity readiness of TVET institutions in Kenya. It analyzed their existing policies, practices, incident response and recovery procedures to identify potential weaknesses and areas for improvement. The research employed a mixed-methods approach, combining qualitative interviews with key stakeholders along with quantitative surveys distributed across multiple TVET institutions. 50 TVET institutions were randomly selected and questionnaires distributed to the Management and IT personnel in these institutions. 39 questionnaires were returned translating to 78% response rate. The design of the questionnaire was based on the four major cybersecurity elements: people, process, policy, and technology. The findings indicate that while some TVET institutions have made progress in developing and implementing cybersecurity policies, many are still lagging, and vulnerabilities remain prevalent. Several factors contribute to these gaps, including the absence of a comprehensive cybersecurity strategy, limited resources and inadequate training programs. This study recommends the development of robust cybersecurity policies, regular risk assessments and the establishment of security awareness programs. As TVET digital landscape continues to evolve, the implementation of effective cybersecurity measures is paramount to guarantee the protection of information and other IT assets.

Problems and Solutions in US Stock Market

This research explores critical challenges facing the U.S. stock market, specifically focusing on the impacts of algorithmic trading-induced market volatility and cybersecurity threats. Algorithmic trading, which utilizes high-speed computer systems to make trades, has increased market efficiency but also introduced risks of extreme price fluctuations and flash crashes. In parallel, the digitalization of financial markets has heightened exposure to cybersecurity threats, posing risks to trading integrity and data security. To address these issues, this study proposes two targeted solutions. For the issue of market volatility driven by algorithmic trading, the research recommends implementing circuit breakers and trading halts. These mechanisms can prevent severe market disruptions by temporarily pausing trading during extreme price movements, allowing the market to stabilize and preventing panic-driven selloffs. For the cybersecurity challenges, the study suggests enhancing information sharing and fostering collaborative defense strategies among financial institutions and regulatory bodies. By encouraging greater collaboration, the financial sector can more effectively identify and counteract cybersecurity threats, reducing vulnerabilities across the market.

The Challenges and Problems of Law in the Welfare State in the Digital Age

The digital era presents significant challenges for the welfare state, particularly in relation to the legal framework. This research aims to identify and analyze the legal challenges and issues faced by the welfare state in the context of rapid technological advancement. Using a descriptive approach, this research employs a literature review of recent sources published in the last decade to ensure their relevance and accuracy. Key challenges identified include data protection and privacy issues, digitalization of social services, cybersecurity threats, and digital workforce regulation. In addition, the study also addresses emerging legal issues such as copyright infringement, money laundering through cryptocurrencies, and the inadequacy of existing cybercrime laws. The findings show that while technological developments offer opportunities to improve welfare services, they also create complexities that threaten equality and social justice. The need for a robust legal framework that can adapt to these changes is emphasized, highlighting the role of the state in protecting citizens' rights in both the physical and digital realms. In conclusion, this research underscores the urgent need for legal reforms that balance the rights of individuals with the well-being of society, ensuring that the benefits of digital technologies are shared equitably across society. This research ultimately contributes to the ongoing discourse on the intersection of law, technology and social welfare in the modern era.

Enhancing Security in International Data Spaces: A STRIDE Framework Approach

The proliferation of Internet of Things (IoT) devices and big data has catalyzed the emergence of data markets. Regulatory and technological frameworks such as International Data Spaces (IDS) have been developed to facilitate secure data exchange while integrating security and data sovereignty aspects required by laws and regulations, such as the GDPR and NIS2. Recently, novel attack vectors have taken a toll on many enterprises, causing significant damage despite the deployed security mechanisms. Hence, it is reasonable to assume that the IDS may be just as susceptible. In this paper, we conduct a STRIDE threat analysis on IDS to assess its susceptibility to traditional and emerging cybersecurity threats. Specifically, we evaluate novel threats such as Man-in-the-Middle (MitM) attacks, compromised end-user devices, SIM swapping, and potential backdoors in commonly used open-source software. Our analysis identifies multiple vulnerabilities, particularly at the trust boundary (TB) between users and the IDS system. These include the traditionally troublesome Denial of Service (DoS) attacks, key management weaknesses, and the mentioned novel threats. We discuss the hacking techniques, tools, and associated risks to the IDS framework, followed by targeted mitigation strategies and recommendations. This paper provides a framework for performing a STRIDE-based threat analysis of the IDS. Using the proposed methodology, we identified the most potent threats and suggested solutions, thus contributing to the development of a safer and more resilient data space architecture.

A Review on Online Voting System using Face Recognition and Blockchain

Online voting systems represent a transformative approach to modernizing electoral processes, offering benefits such as increased accessibility, security, and efficiency. This paper explores the design, implementation, and challenges of online voting systems, with a particular focus on the integration of biometric authentication, encryption, and blockchain technology. These systems aim to address the critical issues of voter identity verification, fraud prevention, and the integrity of election results. The methodology for creating an online voting system involves a multi-stage process, including requirement analysis, system design, development, security implementation, testing, deployment, and post-election auditing. Despite the numerous advantages, challenges such as cybersecurity threats, privacy concerns, and digital accessibility remain. This paper discusses the importance of addressing these challenges to ensure the security, transparency, and inclusivity of online voting. By adopting robust security measures and considering the legal and technological landscape, online voting systems can enhance voter participation and transform electoral practices, providing a reliable and efficient alternative to traditional voting methods

Machine Learning-Based Anomaly Detection Model for Cybersecurity Threat Detection

Machine Learning-Based Anomaly Detection Model for Cybersecurity Threat Detection

Securing the Future of Railway Systems: A Comprehensive Cybersecurity Strategy for Critical On-Board and Track-Side Infrastructure.

The growing prevalence of cybersecurity threats is a significant concern for railway systems, which rely on an extensive network of onboard and trackside sensors. These threats have the potential to compromise the safety of railway operations and the integrity of the railway infrastructure itself. This paper aims to examine the current cybersecurity measures in use, identify the key vulnerabilities that they address, and propose solutions for enhancing the security of railway infrastructures. The report evaluates the effectiveness of existing security protocols by reviewing current standards, including IEC62443 and NIST, as well as case histories of recent rail cyberattacks. Significant gaps have been identified, especially where modern and legacy systems need to be integrated. Weaknesses in communication protocols such as MVB, CAN and TCP/IP are identified. To address these challenges, the paper proposes a layered security framework specific to railways that incorporate continuous monitoring, risk-based cybersecurity modeling, AI-assisted threat detection, and stronger authentication methodologies. The aim of these recommendations is to improve the resilience of railway networks and ensure a safer, more secure infrastructure for future operations.