Enhancing Cyber Security Research Articles

Understanding Penetration Testing for Evaluating Vulnerabilities and Enhancing Cyber Security

In response to the increasing incidence of cyber-attacks, organizations are prioritizing security testing for their software applications and products. Among the most effective methods for identifying vulnerabilities is penetration testing, which involves simulated attacks on systems to uncover weaknesses that could be exploited by malicious actors. This method not only aids in identifying and remediating security flaws but also evaluates a system's ability to withstand unexpected threats. This paper provides an in-depth exploration of penetration testing, detailing its stages, methodologies, and the role of web application firewalls. A penetration test systematically assesses the security of IT infrastructures by exploiting vulnerabilities in systems, applications, and user behaviours. The findings from these tests are essential for IT management, guiding strategic decisions and prioritizing remediation efforts. Ultimately, the primary aim of penetration testing is to evaluate the risk of a system breach and its potential impact on organizational resources and operations.

Cyber threats in the age of artificial intelligence: Exploiting advanced technologies and strengthening cybersecurity

The rapid advancement in artificial intelligence (AI) technologies has led to the emergence of new and complex cyber threats. These threats rely on AI capabilities to target digital systems and infrastructures with greater precision and efficiency, making them more difficult to detect and counter. AI is being used to develop automated cyber-attacks, such as malware that can adapt and learn from its digital environment. Conversely, AI is also employed to enhance cyber security by enabling early threat detection, analyzing suspicious behaviors, and providing rapid responses to hacking incidents. Based on the above, the research idea we intend to present at this article on how to achieve a balance between leveraging AI technologies to protect digital systems and the growing risks associated with using these technologies to develop more sophisticated cyber-attacks. Research Methodologies: The study requires employing both the descriptive-analytical and inferential methodologies.

Enhancing Cyber Security in Autonomous Vehicles: A Hybrid XG Boost-Deep Learning Approach for Intrusion Detection in the CAN Bus

Enhancing Cyber Security in Autonomous Vehicles: A Hybrid XG Boost-Deep Learning Approach for Intrusion Detection in the CAN Bus

The Role of Automation in Enhancing Cybersecurity: a Technical Analysis

Intelligent automation, a convergence of artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA), is reshaping modern business operations across various sectors. This article examines the transformative impact of intelligent automation on organizational efficiency, cost structures, and innovation capabilities. Recent industry surveys indicate a significant increase in adoption rates, with 67% of business leaders implementing intelligent automation at scale in 2023, up from 48% in 2020. The integration of AI and ML with RPA has enabled businesses to automate complex, cognitive tasks, leading to an average 35% reduction in operational costs and a 50% improvement in process cycle times. While challenges such as data privacy, ethical considerations, and workforce reskilling persist, the potential benefits of intelligent automation are substantial. This article explores quantifiable impacts across industries, examines real-world case studies, and anticipates future trends including hyperautomation, autonomous decision-making, and integration with emerging technologies like blockchain and edge computing. The article concludes that strategic implementation of intelligent automation solutions will be crucial for organizations to maintain competitiveness and drive innovation in the increasingly digital business landscape.

The Role of Digital Health Under Taiwan’s National Health Insurance System: Progress and Challenges

ABSTRACT Digital health covers a wide spectrum of applications of digital technologies in the healthcare field. As a new set of tools to support the health system in achieving its goals—improving access to care, quality of care, and system efficiency—digital health has significantly transformed the landscape of modern medicine and health care. This paper examines the role of digital health under Taiwan National Health Insurance, considering the profound impacts of digital health during the COVID-19 pandemic. It focuses specifically on big data management and analytics (MediCloud and My Health Bank/NHI Mobile Easy Access) and innovative service provision models (telemedicine). We discuss two imminent challenges that any health system is likely to encounter: digital trust and digital divide. For the digital divide, we assessed the use of telemedicine and its determinants during the COVID-19 pandemic. Our study shows that high-income levels and the presence of chronic or severe illness were positively correlated with the use of telemedicine. This observation suggests that poor people who have poorer health status were most likely to suffer from unmet needs for telemedicine. Enhancing cybersecurity to safeguard confidentiality, and effective communications with the public are fundamental and essential steps to regaining public trust in the digital era. When calling for more investment in digital technology, policy makers should be mindful of the potential digital divide across the demographic and socioeconomic strata, and specific policies should be devised to provide support to target the socially disadvantaged group.

How can artificial intelligence be used to detect and mitigate zero-day vulnerabilities?

A zero-day exploit is a cyberattack that uses unknown or unaddressed security flaws in computer software, hardware, or firmware. Zero-day vulnerabilities pose very significant threats to cyber security. While traditional methods have been effective, they are lacking in many aspects due to rapidly evolving cyber threats. Hence, this paper examines artificial intelligence techniques, including machine learning and their application in enhancing cyber security against zero-day vulnerabilities. The research delves into supervised and unsupervised models and algorithms like Naive Bayes. The findings suggest that effective solutions such as artificial intelligence-driven approaches are crucial in the face of rapidly evolving cyber threats.

Enhancing Cybersecurity with Safe and Reliable AI: Mitigating Threats While Ensuring Privacy Protection

Enhancing Cybersecurity with Safe and Reliable AI: Mitigating Threats While Ensuring Privacy Protection

Enhancing Cybersecurity Awareness through Gamification: Design an Interactive Cybersecurity Learning Platform for Multimedia University Students

Cybersecurity has emerged as a critical imperative in contemporary digital landscapes, necessitating heightened awareness and proficiency across all demographic segments. Accordingly, this research has been meticulously crafted to delve into the complexities of cybersecurity awareness, with a specific focus on university students. The study embarks on an exhaustive analysis encompassing the evaluation of cybersecurity awareness levels within the targeted groups, the identification of prevailing issues and practices, and an exploration of novel methodologies, notably gamification, to fortify cybersecurity knowledge and skills among diverse user cohorts. Central to this investigation is the efficacy of gamified learning environments tailored expressly for augmenting cybersecurity awareness among university students. Through a comprehensive examination of existing platforms, methodological frameworks, and user interactions, this research outlines critical trends, challenges, and latent opportunities within the cybersecurity awareness domain, with a specific emphasis on gamification's transformative potential. The study not only identifies key areas for improvement but also proposes innovative solutions rooted in gamified learning paradigms, with the overarching goal of fostering engaging, effective, and sustainable cybersecurity awareness initiatives among students. Drawing upon a synthesis of theoretical constructs, empirical insights, and pragmatic recommendations, this research significantly contributes to the evolving discourse on cybersecurity education. By underscoring the transformative efficacy of gamification as a pivotal tool in cybersecurity awareness initiatives, this study overlays the way for substantial advancements in cybersecurity education paradigms, offering a roadmap for enhancing cybersecurity awareness levels among university students and beyond.

English

English

Enhancing Cybersecurity through AI-Powered Security Mechanisms

In the rapidly evolving landscape of digital technology, the proliferation of interconnected systems has brought unprecedented opportunities and challenges. Among these challenges, the escalating frequency and sophistication of cyberattacks pose significant threats to individuals, organizations, and nations. In response, the fusion of Cybersecurity and Artificial Intelligence (AI) has emerged as a pivotal paradigm, offering proactive, intelligent, and adaptable defense mechanisms. This research explores the transformative impacts of AI-powered security on cybersecurity, demonstrating how AI techniques, including machine learning, natural language processing, and anomaly detection, fortify digital infrastructures. By analyzing vast volumes of data at speeds beyond human capacity, AI-driven cybersecurity systems can identify subtle patterns indicative of potential threats, allowing for early detection and prevention. The exploration consolidates existing studies, highlighting the trends and gaps that this research addresses. Expanded results and discussions provide a detailed analysis of the practical benefits and challenges of AI applications in cybersecurity, including case studies that offer concrete evidence of AI's impact. Novel contributions are emphasized through comparisons with other studies, showcasing improvements in accuracy, precision, recall, and F-score metrics, which demonstrate the effectiveness of AI in enhancing cybersecurity measures. The synergy between AI and human expertise is explored, highlighting how AI-driven tools augment human analysts' capabilities. Ethical considerations and the "black box" nature of AI algorithms are addressed, advocating for transparent and interpretable AI models to foster trust and collaboration between man and machine. The challenges posed by adversarial AI, where threat actors exploit AI system vulnerabilities, are examined. Strategies for building robust AI security mechanisms, including adversarial training, model diversification, and advanced threat modeling, are discussed. The research also emphasizes a holistic approach that combines AI-driven automation with human intuition and domain knowledge. As AI continues to rapidly evolve, a proactive and dynamic cybersecurity posture can be established, bolstering defenses, mitigating risks, and ensuring the integrity of our increasingly interconnected digital world.

Impact of digital transformation on financial stability in emerging markets: evidence from Ethiopia

This insightful study delves into the intricate relationship between key digital finance adoption indicators and the stability of the banking sector in Ethiopia, an emerging economy undergoing rapid digitalization. Covering annual data from 1991 to 2022 and utilizing an autoregressive distributed lag (ARDL) approach alongside Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) techniques, the researchers uncover thought-provoking findings that challenge conventional wisdom. Contrary to expectations of a positive long-term association, the analysis reveals a statistically significant negative relationship between both internet usage and mobile phone subscriptions and Ethiopia's banking system's financial stability. This robust result, validated through rigorous robustness checks, suggests that the breakneck pace of digital transformation may outpace the development of robust regulatory frameworks and risk management practices, ultimately undermining the overall stability of the financial sector. Interestingly, the short-term analysis presents a more nuanced picture, with mobile phone subscriptions exhibiting a stabilizing effect in the immediate aftermath. This intriguing dichotomy highlights the complex and evolving dynamics at play, where the challenges of integrating digital finance with the traditional banking infrastructure and the persistence of informal financial activities potentially offset the benefits of enhanced financial inclusion and transaction efficiency. These thought-provoking findings carry crucial implications for policymakers and industry stakeholders navigating the digital revolution. Policy implications emphasize the need for robust regulatory frameworks, enhanced cyber security measures, and improved financial literacy. The findings underscore the importance of a nuanced approach to digital finance in emerging economies, advocating for continuous adaptation of policies to address the evolving landscape of financial technology. Continued inquiry can better guide policies that foster digital transformation while safeguarding stability, especially in rapidly developing regions like sub-Saharan Africa.Graphical

AI-driven Predictive Analytics for Enhancing Cybersecurity in a Post-pandemic World: a Business Strategy Approach

Due to heightened technological advancement that has been ignited by the COVID-19 pandemic, the use of technology in running businesses has surged and therefore making businesses prone to cybersecurity threats. The present paper explores the potential of AI as a tool for advancing cybersecurity in the world after a pandemic, and how business organizations can organize its application for protection against new emerging forms of threats. The research draws on secondary industry data and primary case studies of the businesses with AI-based cybersecurity in place. The outcome demonstrates that emerging AI technologies, including machine learning and neural networks, have cut threat detection time by 40% and enhanced the precision of recognizing potential security threats by 30%, beyond conventional cybersecurity solutions. The new and distinct feature of this paper is to elucidate that new threats can be predicted with the help of AI, while there is a continuous need for new training for AI in relation to cyber threats. Drawing from the literature, this paper, therefore, posits that incorporation of AI-based predictive analytics into business cybersecurity models and systems is not only vital for the defense of data and infrastructure against hackers but equally important for the achievement of sustainable, long-term business continuity during the current day, age of technology and globalization.

Enhancing Cybersecurity: Ransomware Detection - A Proof of Concept Study

Presently, our digital landscape faces a pervasive onslaught of diverse cyber threats, encompassing distributed denial of service (DDoS), phishing, ransomware, and smishing, all orchestrated with malicious intent. Counteracting these malicious incursions poses a formidable challenge, particularly in devising efficacious detection solutions. Ransomware incidents are on the ascent, particularly within critical sectors such as healthcare, finance, and telecommunications. Consequently, this paper introduces a proof of concept (POC) aimed at detecting ransomware activities targeting Internet of Medical Things (IoMT) devices. The primary objective of this paper revolves around the identification and evaluation of factors correlating with ransomware attacks on IoMT and then developing a ransomware detector. The experimental framework involves the utilization of a simulated environment mirroring real-world IoMT devices and networks. The methodology integrates diverse approaches, encompassing data collection from IoMT devices, analysis of ransomware behaviour through the study of encryption patterns, and anomaly detection. The POC assesses the efficacy of these methodologies in detecting and responding to ransomware threats, with the experimentation conducted through hybrid analysis within a controlled laboratory setting. The dataset consists of 13 families with a total malware of 9251 taken from GitHub. For POC, a total of 3459 ransomware dataset samples have been selected. As a result of the experiment, thirteen (13) distinct features have been identified as trigger factors for ransomware attacks. These features are obtained by capturing of the processes initiated by the ransomware samples using a process monitor (procmon). A temporal pattern of the processes which include the file system, API, and access based on their frequency of occurrence were used to develop a ransomware detection model. The proposed approach achieved an accuracy of 99.2% and an error rate of 0.8 %, when evaluated on temporal pattern dataset and by using an enhanced artificial neural network (EANN).

A model proposal for enhancing cyber security in industrial IoT environments

A model proposal for enhancing cyber security in industrial IoT environments

Enhancing cybersecurity protocols in tax accounting practices: Strategies for protecting taxpayer information

This paper highlights the importance of enhancing cybersecurity measures in tax accounting to protect taxpayer data. It proposes strategies to ensure compliance and enhance data security in the context of evolving cyber threats. This study investigated the cybersecurity landscape within the tax accounting sector, focusing on prevalent threats, the effectiveness of existing security measures, and areas for improvement. Utilizing a mixed-methods approach, the research combined qualitative interviews with cybersecurity experts and tax accounting professionals, quantitative surveys of 200 tax accounting firms, and detailed case studies of firms that experienced significant cyberattacks. The findings reveal that phishing attacks, ransomware, data breaches, malware, and insider threats are the most common cybersecurity challenges faced by tax accounting practices. While measures such as encryption, multi-factor authentication (MFA), firewalls, intrusion detection systems (IDS), regular security audits, and comprehensive employee training prove effective, their inconsistent implementation across the industry highlighted the need for standardized protocols. The study identified significant gaps in resource allocation, particularly for smaller firms and non-profits, and underscores the necessity for formal incident response plans. Recommendations include enhanced training programs, development of standardized security protocols, resource support for smaller firms, regular security audits, comprehensive incident response plans, and adoption of advanced technologies. The study calls for further exploration of emerging threats, cost-effective solutions for smaller firms, the impact of artificial intelligence (AI) and machine learning (ML), longitudinal studies on cybersecurity practices, and analysis of policy and regulatory impacts. These insights aim to enhance the cybersecurity posture of tax accounting practices, ensuring the protection of sensitive taxpayer information and overall industry resilience.

A Mathematical Model for Enhancing Cybersecurity in IoT Networks Using LSTM-Based Anomaly Detection and Optimization

A Mathematical Model for Enhancing Cybersecurity in IoT Networks Using LSTM-Based Anomaly Detection and Optimization

Zero Trust Architecture: Enhancing Cybersecurity in Enterprise Networks

Zero Trust Architecture (ZTA) offers a robust approach to enhancing cybersecurity in enterprise networks, replacing the traditional perimeter-based security models. This paper examines the application, challenges, and effectiveness of ZTA in contemporary corporate environments, with a focus on hybrid and cloud infrastructures. By emphasizing key principles such as least-privileged access, continuous authentication, and network segmentation, ZTA directly addresses the security risks that organizations face today. This research includes a review of relevant literature and an analysis of case studies to explore the difficulties companies encounter when adopting ZTA, including financial costs, integration complexities, and resistance to change. The study also identifies strategies that organizations have successfully employed to overcome these obstacles, leading to improved security and operational efficiency. The findings highlight ZTA’s ability to reduce security incidents through automation and enhanced monitoring. While there are technical challenges to implementing Zero Trust, the research concludes that the framework is essential for maintaining a strong security posture. Future areas for exploration include the role of technologies like artificial intelligence and machine learning in further improving ZTA.

Enhancing cybersecurity: A comprehensive approach to addressing the growing threat of cybercrime

The rise of technology and internet usage has revolutionized interactions and business practices but has also significantly increased the prevalence of cybercrime. Cybercrimes, including data theft, ransomware, attacks on critical systems, and botnet-driven attacks, have grown more sophisticated and damaging. Recent research by Mphatheni and Maluleke (2022), Cascavilla, Tamburri, and Heuvel (2021), Shah and Chudasama (2021), Dupont and Whelan (2021), Djenna et al. (2023), and Back and LaPrade (2020) offers a detailed analysis of contemporary cybersecurity challenges and emerging solutions. These studies emphasize the need for a comprehensive approach to combating cybercrime. Mphatheni and Maluleke (2022) highlight the absence of a universal definition for cybercrime, which undermines prevention efforts and overlooks the global economic impact, particularly in Africa. Cascavilla, Tamburri, and Heuvel (2021) stress the importance of advanced machine learning techniques and threat intelligence for detecting cybercrimes across various web layers, advocating for improved risk assessment and anonymity measures. Shah and Chudasama (2021) propose a new cybercrime taxonomy to address gaps in current legislation, especially in regions like India where crimes often stem from data issues or malicious intent. Dupont and Whelan (2021) call for greater integration between cybercriminology and cybersecurity, promoting a continuous approach that fosters better collaboration between security and crime control domains. Djenna et al. (2023) introduce an advanced deep learning method for early botnet attack detection, enhancing forensic investigations and threat response. Back and LaPrade (2020) emphasize updating cybersecurity practices in academic institutions using a Situational Crime Prevention (SCP) framework to improve digital security. Overall, a proactive and coordinated strategy, incorporating new technologies, enhanced practices, and updated legislation, is essential for effective cybercrime prevention and response. Collaboration among governments, the private sector, and academia is crucial for developing robust defenses against escalating cyber threats.

Enhancing Cybersecurity in Healthcare: Evaluating Ensemble Learning Models for Intrusion Detection in the Internet of Medical Things.

This study investigates the efficacy of machine learning models for intrusion detection in the Internet of Medical Things, aiming to enhance cybersecurity defenses and protect sensitive healthcare data. The analysis focuses on evaluating the performance of ensemble learning algorithms, specifically Stacking, Bagging, and Boosting, using Random Forest and Support Vector Machines as base models on the WUSTL-EHMS-2020 dataset. Through a comprehensive examination of performance metrics such as accuracy, precision, recall, and F1-score, Stacking demonstrates exceptional accuracy and reliability in detecting and classifying cyber attack incidents with an accuracy rate of 98.88%. Bagging is ranked second, with an accuracy rate of 97.83%, while Boosting yielded the lowest accuracy rate of 88.68%.

Artificial Intelligence in Cybersecurity Threat Detection

With the increasing frequency and complexity of cyberattacks, traditional cybersecurity threat detection methods have been difficult to cope with new types of threats. Artificial Intelligence (AI) technology, with its powerful data processing and pattern recognition capabilities, has gradually become an important tool for enhancing cyber security. This paper aims to explore the application of AI in cybersecurity threat detection, firstly outlining the current status of the development of AI technology in cybersecurity, and then focusing on analyzing the application of core methods such as machine learning and deep learning in threat detection, and discussing the advantages of integrated learning and multimodal methods. Finally, this paper summarizes the current challenges faced by AI technology in the field of cyber security and looks forward to the future development direction. Through the research in this paper, it is expected to provide reference for improving the accuracy and efficiency of cybersecurity threat detection.