Cybersecurity Threats Research Articles

Adaptive Vulnerability Matching Assessment: A Holistic Approach for Cyber security Resilience

Abstract: In the rapidly evolving landscape of cyber security threats, organizations face the critical challenge of identifying and mitigating vulnerabilities to protect their digital assets. Organizations are left vulnerable to possible security incidents and breaches because traditional vulnerability assessment approaches frequently cannot keep up with the ever-changing nature of cyber threats. To address this challenge, we present Vulnerability Matching assessment, a comprehensive framework designed to enhance cyber security resilience through adaptive vulnerability management. The aim is to provide an efficient and userfriendly solution for identifying and addressing security vulnerabilities, which will ultimately enhance the overall security posture of the scanned software or systems. The program will provide users with comprehensive reports so they can fix the vulnerabilities found.

The Role of International Law in Addressing Transnational Cybersecurity Threats: Challenges and Opportunities

As the world becomes increasingly interconnected, the proliferation of cyber threats poses significant challenges to global security and stability. the role of international law in addressing transnational cybersecurity threats and explores the challenges and opportunities associated with its application in this context. Drawing on existing legal frameworks and case studies, the paper analyzes the effectiveness of international law in regulating cyberspace and mitigating cyber risks. Furthermore, it explores the evolving nature of cyber threats and the need for adaptive legal responses to address emerging challenges. By examining the intersection of international law and cybersecurity, this paper seeks to contribute to a deeper understanding of the complexities surrounding cyber governance and the potential avenues for enhancing global cybersecurity cooperation.

Analysis of Behavioral Acceptance and Security Behavior on Implementation of Digital Workplace

The creative industry is growing and accelerating, predicted to be a future of national economic power. The use of the digital workplace in many organizations represents a digital transformation of the work environment through cloud-based productivity tools that facilitate collaboration among companies and employees. Implementing the digital workplace can enhance efficiency and effectiveness in highly dynamic work that requires agility. However, there are risks and potential negatives to the prevalence of cybersecurity threats and incidents. The causes and impacts are interrelated and influenced by user behavior at the individual and organizational levels. This phenomenon is studied and evaluated in detail to understand how behavioral factors and patterns affect the acceptance and security of the digital workplace. The study involved 402 respondents from 13 job positions in 27 business fields in the Application Development Sector. It was examined and evaluated using the UTAUT model and the Security Behavior Model with SEM methodology. The results showed a significant correlation between exogenous variables and endogenous variables such as security behavior, use behavior, and continued usage. The only exception was the perceived security threat, which did not correlate with security behavior. This research provides implications and contributions through strategies for implementing and enhancing the digital workplace as an easily accepted and secure platform to support organizational productivity

Enhancing ransomware detection using Siamese network

Organizations in the current digital era are exposed to a variety of cybersecurity threats that can often result in financial losses and harm to their reputation. Among these threats, ransomware attacks can cause significant damage. Attackers are constantly improving their techniques to bypass security channels, which makes it challenging to monitor and detect the patterns of attacks. Consequently, there is a growing inclination towards employing state-of-the-art techniques to identify and defend during ransomware attacks. Deep learning is a proven technique that can be employed to learn from large complex patterns. However, large datasets are required in the training of deep learning models which is a challenging task. Few-shot learning (FSL) overcomes this limitation by using less data. In this research work, a Siamese network design is developed by incorporating the architectural principles of AlexNet and features of the VGG configuration. The employed methodology enables us to evaluate the inherent resemblances and disparities in the data. This novel methodology demonstrated exceptional performance, with an average accuracy of 97% when compared to various effects and learning rates. The results of the presented study demonstrate the capacity to greatly enhance cybersecurity by providing a scalable and effective approach for detecting ransomware.

Research on the Impact of Technological Innovation on the Financial Services Industry

This study provides an in-depth examination of the profound impact technological innovation has on the financial services industry, focusing on recent advancements in blockchain, artificial intelligence (AI), machine learning (ML), big data analytics, and fintech solutions. It delves into the nuanced ways these technologies revolutionize operational efficiency, enhance customer experience, facilitate the development of innovative financial products, and navigate the complex landscape of regulatory compliance. Through a combination of qualitative case studies and quantitative analysis of industry performance metrics, this research uncovers the nuanced dynamics between technological adoption and its effects on market competitiveness, security, and the democratization of financial services. Key findings highlight a dual-edged influence: while technological innovation drives unprecedented efficiencies and customer value creation, it also introduces challenges such as increased cybersecurity threats, privacy concerns, and a growing digital divide among consumers. Additionally, the study sheds light on the evolving role of human capital in an increasingly automated industry and the critical importance of strategic digital literacy and adaptive regulatory frameworks to foster sustainable growth.

Cybersecurity Law: Challenges and Legal Frameworks for Protecting Digital Assets and Privacy Rights

Provides an overview of the complex landscape of cybersecurity law, highlighting the challenges and legal frameworks aimed at safeguarding digital assets and privacy rights in an increasingly interconnected and digital world. the evolving nature of cybersecurity threats, the legal principles underpinning cybersecurity regulation, and the tensions between security imperatives and individual rights. Cybersecurity law encompasses a broad range of legal principles, regulations, and policies designed to protect digital assets, information systems, and privacy rights from cyber threats and attacks. the multifaceted nature of cybersecurity challenges, including data breaches, malware attacks, ransomware, insider threats, and state-sponsored cyber espionage, which pose significant risks to individuals, organizations, and governments worldwide.

IMPLEMENTATION OF DYNAMIC METHOD FOR MALWARE DETECTION IN EMAIL PHISHING ATTACKS ON LET'S DEFEND

In the rapidly evolving landscape of cybersecurity threats, the need for robust defenses against phishing attacks has become paramount. This study explores the efficacy of malware detection in phishing email attacks using dynamic analysis on the Letsdefend.io platform. Leveraging the insights provided by the Deloitte Center for Controllership regarding the escalating frequency of cyber adversaries targeting organizational data, this research investigates the effectiveness of the Letsdefend.io platform, particularly utilizing the SOC 146 rule, in identifying and mitigating phishing threats. Through a comprehensive analysis process encompassing dynamic malware analysis techniques, such as those employed by VirusTotal and URLHaus, alongside detailed examination of suspicious email attachments using the Mailbox feature, this study aims to provide insights into the evolving tactics of phishing attackers, specifically those utilizing Excel 4.0 Macros. The research methodology involves collecting malware samples for analysis, configuring sandbox environments with tools like Process Monitor and Regshot, and utilizing sophisticated analysis tools like ProcDot to visualize malware behavior. Additionally, the study examines the effectiveness of the Letsdefend.io platform in detecting phishing URLs and malicious domains reported by AnyRun and URLHaus databases. The findings reveal promising results in the detection and identification of phishing threats, shedding light on the potential of dynamic analysis methods in bolstering cybersecurity defenses against evolving phishing techniques. This research contributes to the ongoing efforts to enhance cybersecurity measures and protect organizational assets from the pervasive threat of phishing attacks.

Using Artificial Intelligence to Evaluating Detection of Cybersecurity Threats in Ad Hoc Networks

Using Artificial Intelligence to Evaluating Detection of Cybersecurity Threats in Ad Hoc Networks

A Review on Detection of Cybersecurity Threats in Banking Sectors Using Ai Based Risk Assessment

The continued rise of cybercrime in the current era has frightened not only off-users but also violating financial institutions. The recent studies have shown that most financial institutions lose the credibility of their clients and their investments as a result of these attacks. The cyber-hackers involved in these types of cybercrime are now using more advanced technology. Its vigour surpasses the firewall system currently in use our network. Hence, the security needs for financial institutions are increased. So, the server management and cyber security systems getting higher end security. The cyber security aspects are is always shifting based on the attacks, but the cyber security threats are increased and create the serious discussion about the financial institutions. In General, the cyberspace describes the combination of all types of communication networks, databases, embedded processors, Internet and controllers for exchanging electronic documents. The global network environment is created by telephone wires, coaxial cables, electromagnetic waves and fiber-optic lines. Although the terms cyberspace and the Internet can be used interchangeably, it is still part of cyberspace. In simple terms, cyberspace is a connected web environment. Now, cyber-security is the process of protecting cyberspace from attack, misuse, damage and economic espionage. Cyberspace can sometimes be blocked by intrinsic vulnerabilities that cannot be eliminated. In this study, different types of cyber cards are explained in detail. And the escape processes from certain subtle attack methods are clearly stated.

Predicting Cyber Security Threats in The Digital Age: A Systematic Review and Analysis

Insider threats pose a significant challenge in securing today's digital economy, particularly within the convergence of Critical Infrastructure (CI) and Industry 4.0 applications. This study examines current trends, challenges, and algorithms for detecting and predicting insider threats. We analyse key issues such as data non-stationarity and collusion attacks. The paper proposes a model to address these challenges and evaluates its effectiveness. While the initial results show a 66% accuracy in terms of False Positive Rate (FPR), the study recommends doubling the training data size to further enhance the model's prediction accuracy. This research contributes to improved security solutions for safeguarding critical infrastructure in the era of Industry 4.0. In today's digital world, even simple mistakes when using online systems can lead to cyberattacks with serious consequences. These consequences can include damage to an organization's reputation, financial losses, and disruptions to daily operations. This problem is especially concerning in developing countries. Researchers tackled this issue by building a machine learning model that predicts who might fall victim to cyberattacks. The model analyses social and economic data to identify key factors that make someone more susceptible. This model achieved a high level of accuracy by utilizing a sophisticated machine learning technique combined with an algorithm that discovers hidden patterns in data. To train the model, researchers collected information from both victims and those who haven't been attacked. They then employed a special technique to expand the data available for training, ultimately generating valuable insights that can help predict cyberattacks and the associated risks.

IDENTIFICATION AND PROACTIVE PREVENTION OF PHISHING WEBSITES

The project titled "Identification and Proactive Prevention of Phishing Websites" aims to address the growing cybersecurity threat posed by phishing attacks. This project focuses on developing effective strategies for both the identification and prevention of phishing websites. The identification aspect involves the implementation of algorithms and tools to analyze website URLs for potential indicators of phishing, such as misspellings or subtle variations. The project aims to enhance user awareness and provide real- time feedback on the legitimacy of websites, empowering users to make informed decisions when navigating the online environment.

Risk Mitigation and Strategies in Aviation

In today's dynamic and uncertain business environment, organizations face countless risks that can significantly affect their operations, finances and reputation. The spectrum of risks is broad and constantly evolving, from natural disasters and economic downturns to cyber security threats and regulatory changes. Effective risk management is essential for organizations to manage these uncertainties and protect

Beyond Conventional Threat Defense: Implementing Advanced Threat Modeling Techniques, Risk Modeling Frameworks and Contingency Planning in the Healthcare Sector for Enhanced Data Security

This research study explores the integration of advanced threat and risk modeling with scenario and contingency planning to enhance security and resilience within healthcare organizations. Given the escalating complexity and frequency of cyber threats targeting the healthcare sector, the study assesses the efficacy of a synergistic cybersecurity approach. Utilizing a quantitative research methodology, data were collected through a survey administered to 452 healthcare practitioners, focusing on their perceptions and experiences with cybersecurity threats, risk modeling techniques, and the effectiveness of scenario and contingency planning. The survey incorporated a structured questionnaire utilizing Likert scale closed-ended questions, analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the proposed four hypotheses related to the impact of advanced threat and risk modeling and scenario and contingency planning on the cybersecurity posture and operational resilience of healthcare organizations. Results indicated a significant positive relationship between advanced threat modeling and scenario planning with the cybersecurity posture of healthcare organizations, which, in turn, notably enhances organizational resilience. Specifically, the study found that integrating advanced threat and risk modeling with scenario and contingency planning significantly improves the ability of healthcare organizations to identify, assess, and mitigate cyber threats effectively. Furthermore, the findings suggest that such integration contributes to more informed decision-making, improved information security management practices, and more effective and efficient digital crime investigation processes. The research study concludes that a comprehensive approach integrating advanced threat and risk modeling with scenario and contingency planning significantly enhances healthcare organizations' cybersecurity posture and operational resilience. The study recommends adopting an integrated cybersecurity strategy, continuous cybersecurity education for all organizational members,regular cybersecurity assessments, and fostering collaboration for enhanced cybersecurity intelligence.

ECONOMIC THEORY AND PRACTICAL IMPACTS OF DIGITAL TRANSFORMATION IN SUPPLY CHAIN OPTIMIZATION

Digital transformation has emerged as a critical driver of innovation and efficiency in supply chain management, reshaping traditional economic paradigms and yielding profound practical implications. This paper delves into the economic theory underpinning digital transformation in supply chain optimization and explores its practical impacts on businesses and industries. In terms of economic theory, we examine the evolution from traditional supply chain models to digitally-enabled ecosystems. The integration of digital technologies such as big data analytics, artificial intelligence, and blockchain revolutionizes supply chain dynamics, enhancing efficiency, reducing costs, and mitigating risks. Drawing on game theory applications, we elucidate strategic interactions among supply chain stakeholders and the implications of digital transformation on their decision-making processes. Turning to practical impacts, we investigate how digital transformation enables enhanced visibility, real-time tracking, and predictive analytics, fostering agility and responsiveness in supply chain operations. Automation and AI-driven insights empower decision-makers to optimize resource allocation and mitigate disruptions effectively. Through case studies, we illustrate the tangible benefits and strategic advantages accrued by organizations embracing digital transformation. However, this transformation is not devoid of challenges. Cybersecurity threats, data privacy concerns, and the integration complexities of legacy systems pose significant hurdles. Addressing these challenges necessitates a holistic approach encompassing technological innovation, regulatory frameworks, and workforce development. Looking ahead, future trends such as blockchain integration, IoT applications, and sustainability initiatives promise further advancements in supply chain optimization. This paper underscores the imperative for ongoing research and adaptation to harness the full potential of digital transformation in reshaping the future of supply chain management. Keywords: Economic Theory, Practical Impacts, Digital Transformation, Supply Chain Optimization.

Risk management strategies for smart hotel supply chain

<p>In the dynamic and interconnected world of hospitality, smart hotel supply chains play a pivotal role in maintaining operational efficiency and service excellence. However, these supply chains are susceptible to many risks, including transportation disruptions, supplier insolvencies, and cybersecurity threats, which can severely impact business continuity and degrade service quality. This conceptual paper proposes a holistic framework for identifying, assessing, and mitigating supply chain risks within the context of smart hotels. By integrating advanced technologies and strategic management practices, this study aims to reinforce smart hotel supply chains against potential vulnerabilities, ensuring sustainable business operations and superior guest satisfaction. The paper explores various risk management measures and leverages supply chain management, hospitality, and cybersecurity insights to develop a robust risk management strategy tailored for smart hotels.<strong></strong></p>

THEORETICAL PERSPECTIVES ON DIGITAL TRANSFORMATION IN FINANCIAL SERVICES: INSIGHTS FROM CASE STUDIES IN AFRICA AND THE UNITED STATES

This review paper explores the theoretical perspectives on digital transformation in financial services, with a focus on insights from Africa and the United States. It delves into key theories and models such as the Diffusion of Innovations Theory, the Technology Acceptance Model (TAM), and the Unified Theory of Acceptance and Use of Technology (UTAUT), and their application to understanding the adoption and impact of digital technologies in financial services. The paper highlights the challenges faced by financial institutions, including regulatory hurdles, cybersecurity threats, and technological disparities, while also discussing the transformative opportunities digital transformation presents for enhancing service delivery and financial inclusion. A comparative analysis reveals how these theoretical frameworks apply across different regions, emphasizing the need for continuous theoretical development to navigate the evolving digital financial landscape. Keywords: Digital Transformation, Financial Services, Theoretical Perspectives, Comparative Analysis.

The Role of Artificial Intelligence in Cyber Security

The research paper explores the pivotal role of artificial intelligence (AI) in bolstering cyber security threat detection and protection measures. By leveraging advanced AI techniques such as machine learning, deep learning, and behavioral analysis, organizations can effectively identify and mitigate cyber threats in real-time. AI enables proactive threat detection by analyzing vast amounts of data, detecting patterns, and predicting potential security incidents. Moreover, AI-driven security solutions offer scalability, efficiency, and adaptability, making them indispensable in combating the evolving cyber threat landscape. Overall, AI plays a crucial role in fortifying cyber security defenses and safeguarding critical assets against sophisticated cyberattacks. As cyber threats continue to grow in complexity and frequency, the integration of AI into security frameworks will not only enhance response times but also empower organizations to stay one step ahead of potential attacks Furthermore, as organizations increasingly adopt AI-driven security measures, the importance of integrating ethical considerations and addressing potential biases in AI algorithms becomes paramount. While these technologies can significantly enhance threat detection capabilities, they also risk perpetuating existing inequalities if not meticulously designed and monitored. For instance, biased data sets may lead to disproportionate scrutiny of certain user groups, undermining trust in security systems. Additionally, the dynamic nature of cyber threats necessitates continuous learning and adaptation from AI systems, highlighting the need for ongoing research and collaboration among cybersecurity professionals, technologists, and ethicists alike. By fostering a holistic approach that emphasizes both technological advancement and ethical responsibility, organizations can better navigate the complexities of modern cyber environments while enhancing their overall security posture

AI-powered malware detection with Differential Privacy for zero trust security in Internet of Things networks

The widespread usage of Android-powered devices in the Internet of Things (IoT) makes them susceptible to evolving cybersecurity threats. Most healthcare devices in IoT networks, such as smart watches, smart thermometers, biosensors, and more, are powered by the Android operating system, where preserving the privacy of user-sensitive data is of utmost importance. Detecting Android malware is thus vital for protecting sensitive information and ensuring the reliability of IoT networks. This article focuses on AI-enabled Android malware detection for improving zero trust security in IoT networks, which requires Android applications to be verified and authenticated before providing access to network resources. The zero trust security model requires strict identity verification for every entity trying to access resources on a private network, regardless of whether they are inside or outside the network perimeter. Our proposed solution, DP-RFECV-FNN, an innovative approach to Android malware detection that employs Differential Privacy (DP) within a Feedforward Neural Network (FNN) designed for IoT networks under the zero trust model. By integrating DP, we ensure the confidentiality of data during the detection process, setting a new standard for privacy in cybersecurity solutions. By combining the strengths of DP and zero trust security with the powerful learning capacity of the FNN, DP-RFECV-FNN demonstrates the ability to identify both known and novel malware types and achieves higher accuracy while maintaining strict privacy controls compared with recent papers. DP-RFECV-FNN achieves an accuracy ranging from 97.78% to 99.21% while utilizing static features and 93.49% to 94.36% for dynamic features of Android applications to detect whether it is malware or benign. These results are achieved under varying privacy budgets, ranging from ϵ=0.1 to ϵ=1.0. Furthermore, our proposed feature selection pipeline enables us to outperform the state-of-the-art by significantly reducing the number of selected features and training time while improving accuracy. To the best of our knowledge, this is the first work to categorize Android malware based on both static and dynamic features through a privacy-preserving neural network model.

An improved transformer‐based model for detecting phishing, spam and ham emails: A large language model approach

AbstractPhishing and spam have been a cybersecurity threat with the majority of breaches resulting from these types of social engineering attacks. Therefore, detection has been a long‐standing challenge for both academic and industry researcher. New and innovative approaches are required to keep up with the growing sophistication of threat actors. One such illumination which has vast potential are large language models (LLM). LLM emerged and already demonstrated their potential to transform society and provide new and innovative approaches to solve well‐established challenges. Phishing and spam have caused financial hardships and lost time and resources to email users all over the world and frequently serve as an entry point for ransomware threat actors. While detection approaches exist, especially heuristic‐based approaches, LLMs offer the potential to venture into a new unexplored area for understanding and solving this challenge. LLMs have rapidly altered the landscape from business, consumers, and throughout academia and demonstrate transformational potential to profoundly impact the society. Based on this, applying these new and innovative approaches to email detection is a rational next step in academic research. In this work, we present IPSDM, an improved phishing spam detection model based on fine‐tuning the BERT family of models to specifically detect phishing and spam emails. We demonstrate our fine‐tuned version, IPSDM, is able to better classify emails in both unbalanced and balanced datasets. Moreover, IPSDM consistently outperforms the baseline models in terms of classification accuracy, precision, recall, and F1‐score, while concurrently mitigating overfitting concerns.

The Impact of Mobility solution on logistics and supply chain

The emergence of mobility solutions, which include changes in customer expectations and technological advancements, is driving a substantial transformation in the transportation and logistics sector. These technologies, which include blockchain, the Internet of Things (IoT), and artificial intelligence (AI), have the revolutionary potential to update conventional wisdom and improve productivity, sustainability, and creativity all the way up the supply chain. However, there are some obstacles and difficulties in the way of their general acceptance. Seamless integration is impeded by regulatory complications, which are typified by inconsistent regulations and uncertainty surrounding emerging technology. Scalability and efficacy are hindered by infrastructure constraints, which might range from insufficient digital connectivity to differences in urban-rural areas. Adoption attempts are further hampered by technology limitations including cybersecurity threats and interoperability problems. Policymakers, industry stakeholders, and researchers must work together to develop collaborative solutions in order to overcome these obstacles and enable widespread adoption. Streamlining regulations, establishing standards, investing in infrastructure, promoting open standards, offering financial incentives, interacting with the community, and attending to social and ethical issues are a few of these. By putting these tactics into practice, the transportation and logistics industry can realize the full potential of mobility solutions and spur expansion, sustainability, and competitiveness. Stakeholders can overcome obstacles, seize opportunities, and clear the path for a transportation ecosystem that is more effective, robust, and inclusive by embracing innovation and teamwork.