Cybersecurity Systems Research Articles

An Enhanced Data Privacy and Security Mitigation Technique: A Novel Federated Deep Learning (FDL) Model for Intrusion detection and Classification System For Cyber -Physical Systems in Internet of things (IoTs)

The Internet of Things (IoT) has faced difficulties in its adoption because of security and data privacy issues that exist in the modern technological environment. Modern cybersecurity systems face multiple problems due to the fast-paced development of cyber threats. The research examines the issue where modern breach methods surpass traditional defense systems. Through federated learning, multiple clients train global models together by sharing machine learning without having to exchange actual data. The document emphasized that security functions as a vital component throughout End-to-End data security configurations. FL operates as an autonomous framework that provides improved data security through decentralized IDS training mechanisms implemented across separate connected devices. This paper analyzes Federated Learning methods and Virtual security protocols which demonstrate their vital role in modern networking infrastructure for establishing secure data transfer on unsecured internet networks. The paper explores the new difficulties that confront Machine Learning model implementations. These techniques help analyze large datasets from IoT devices that connect to the internet due to their effectiveness in processing internet-based application data. A Federated CNN model will serve as an effective system in the future to detect cyber threat identification.

TransECA-Net: A Transformer-Based Model for Encrypted Traffic Classification

Encrypted network traffic classification remains a critical component in network security monitoring. However, existing approaches face two fundamental limitations: (1) conventional methods rely on manual feature engineering and are inadequate in handling high-dimensional features; and (2) they lack the capability to capture dynamic temporal patterns. This paper introduces TransECA-Net, a novel hybrid deep learning architecture that addresses these limitations through two key innovations. First, we integrate ECA-Net modules with CNN architecture to enable automated feature extraction and efficient dimension reduction via channel selection. Second, we incorporate a Transformer encoder to model global temporal dependencies through multi-head self-attention, supplemented by residual connections for optimal gradient flow. Extensive experiments on the ISCX VPN-nonVPN dataset demonstrate the superiority of our approach. TransECA-Net achieved an average accuracy of 98.25% in classifying 12 types of encrypted traffic, outperforming classical baseline models such as 1D-CNN, CNN + LSTM, and TFE-GNN by 6.2–14.8%. Additionally, it demonstrated a 37.44–48.84% improvement in convergence speed during the training process. Our proposed framework presents a new paradigm for encrypted traffic feature disentanglement and representation learning. This paradigm enables cybersecurity systems to achieve fine-grained service identification of encrypted traffic (e.g., 98.9% accuracy in VPN traffic detection) and real-time responsiveness (48.8% faster than conventional methods), providing technical support for combating emerging cybercrimes such as monitoring illegal transactions on darknet networks and contributing significantly to adaptive network security monitoring systems.

Water System Cybersecurity after the Microsoft–CrowdStrike Disruption of July 2024

Water System Cybersecurity after the Microsoft–CrowdStrike Disruption of July 2024

IMPORTANT ISSUES OF DATA PROCESSING AND PROTECTION IN DISTRIBUTED SYSTEMS

The purpose of this article is to explore the critical aspects of big data processing in distributed networks as a contemporary challenge in ensuring cybersecurity. Special attention is given to issues of confidentiality, integrity, and availability of data in multi-user environments with high interaction complexity. The methodology of the study involves a comprehensive approach, including the analysis of threats and vulnerabilities specific to distributed architectures such as cloud technologies, peer-to-peer networks, and decentralized infrastructures. Key issues such as asynchronous data access, limited computational resources, operation synchronization complexity, and risks of data interception during transmission are examined. The study evaluates modern cryptographic approaches, including homomorphic encryption, distributed key management mechanisms, and privacy-preserving protocols. Additionally, the application of machine learning and deep learning algorithms is explored for anomaly detection in system behavioral models in real-time. The scientific novelty of the research lies in the systematization of threats and vulnerabilities inherent to distributed data processing systems and the development of effective approaches to neutralize them. For the first time, a comprehensive approach to the use of blockchain technologies is proposed as a means of ensuring transparent auditing of events and tracking transactions in distributed environments. The conclusions provide practical recommendations for integrating modern cryptographic methods, distributed key management mechanisms, blockchain technologies, and machine learning algorithms to build resilient cybersecurity systems. The findings are of practical significance for ensuring the protection of big data in distributed networks and improving the effectiveness of responses to potential threats.

Adversarial Machine Learning in Cybersecurity: Attacks and Defenses

Adversarial Machine Learning (AML) refers to the research field that involves testing and improving machine learning models by introducing adversarial samples or attack techniques. In the cybersecurity domain, AML has significant potential to help identify and defend against threats such as malware, cyber attacks, and identity fraud. However, AML also faces numerous challenges, including low efficiency in generating adversarial samples, insufficient stealth, and issues with the generality and adaptability of defense methods. There is a dynamic interplay between adversarial attacks and defenses, with attackers continually developing new techniques and defenders needing to constantly improve their defense strategies. This interaction drives the rapid development of AML technology, making it increasingly important in cybersecurity. By deeply studying the interplay between adversarial attacks and defenses, the robustness and reliability of cybersecurity systems can be effectively enhanced, laying the foundation for future AI development in cybersecurity.

Simulation and Testing of Autonomous Cybersecurity Systems: Methodologies for Simulating Cyber-Attacks in Space to Test Effectiveness and Human Interactions

The complexities of modern space missions have intensified the critical need for robust cybersecurity frameworks, particularly as operations become increasingly reliant on autonomous systems to safeguard against an ever-evolving landscape of cyber threats. This study presents a comprehensive investigation into the methodologies for simulating cyber-attack scenarios within the unique constraints of space environments, aiming to evaluate the effectiveness of autonomous cybersecurity systems (ACS) and human-machine collaboration under stress. Space environments pose unparalleled challenges, such as communication latency, limited bandwidth, and the high stakes of mission-critical operations, which require innovative approaches to cybersecurity. Our research introduces a multi-layered simulation framework that integrates advanced artificial intelligence (AI) and machine learning (ML) technologies to model and assess attack vectors including malware infiltration, denial-of-service (DoS) attacks, and insider threats. Real-world mission data informs the design principles, ensuring high fidelity and operational relevance, while scalability and adaptability are prioritized to accommodate a range of mission profiles and evolving adversarial tactics. This work also explores the critical role of human operators within autonomous defense systems, analyzing cognitive load, decision-making processes, and the interplay of trust in automation during high-pressure scenarios. By employing rigorous testing protocols and diverse metrics, including system detection rates, response times, and human interaction efficiency, the findings illuminate both the strengths and limitations of current ACS technologies. The study highlights the necessity for dynamic, modular architectures capable of adapting to new threats and mission requirements, as well as user-centered interface designs that mitigate cognitive overload. Furthermore, it underscores the importance of iterative testing and continuous refinement in aligning ACS capabilities with the unique demands of space operations. This research contributes a foundational framework for advancing cybersecurity resilience in space, offering valuable insights for practitioners, researchers, and stakeholders in an era of unprecedented digital inter connectivity and autonomous system dependency.

Impact of Cyber security Measures on Risk Mitigation, with the Mediating Role of Data Protection

This research paper considers how cybersecurity and data protection method influence the reduction of risk to a company. It also considers how technologies like firewalls, encryption, and multi-factor authentication work to reduce the incidence of cyber-attacks and data leaks. Scientists have also stepped into thinking about the use of data protection to make the various cybersecurity procedures more effective. Stemming from a quantitative research paradigm, authors distributed questionnaires to IT managers together with cybersecurity specialists as well as data protection officers within various sectors. This paper examines the correlation between cybersecurity measures, data security and subsequent risk reduction by employing regression. In total, it is possible to conclude that comprehensible data protection policies, as well as cybersecurity measures, contribute to the reduced levels of risks for an organization. Also, it is supposed that data protection contributes to the success of cybersecurity actions, and thus it is proof of how these two disciplines are inseparable and should be used as two integrated components of the risk management system. The work done in this study reveals that corporations with proper data security measures have greater chances of handling cyber dangers and minimizing operational risks. That these results bear significant implications for policymakers and the business aiming at improving cybersecurity systems and data protection techniques.

Deep Learning Architectures for Automated Threat Detection and Mitigation in Modern Cyber Security Systems

In an era where cyberattacks are growing in scale and sophistication, traditional security mechanisms are increasingly unable to cope with the complexity of modern cyber threats. The advent of deep learning has introduced promising opportunities for enhancing the effectiveness of cybersecurity systems by leveraging advanced computational architectures to automate threat detection and mitigation. This paper explores the design and application of deep learning architectures tailored for modern cybersecurity challenges, emphasizing their role in improving the accuracy, speed, and adaptability of threat detection processes. The study begins by examining the limitations of conventional cybersecurity techniques, including their reliance on static rule-based systems and their inability to process large-scale, diverse, and dynamic data. By contrast, deep learning models, particularly convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based architectures, are capable of extracting meaningful patterns and insights from vast and complex datasets. This capability makes them highly suited for identifying subtle anomalies and previously unknown attack signatures. The paper highlights key advancements in the deployment of deep learning for cybersecurity applications, focusing on intrusion detection systems (IDS), malware classification, phishing detection, and network traffic analysis. It discusses the integration of supervised, unsupervised, and reinforcement learning techniques for creating adaptive systems that not only detect threats in real time but also learn and evolve to counter novel attack vectors. The use of generative adversarial networks (GANs) for adversarial training is also explored as a means to enhance the resilience of cybersecurity systems against evasion attacks. A significant portion of the study is devoted to presenting a novel deep learning-based framework that combines feature extraction, anomaly detection, and automated mitigation strategies. The proposed architecture employs a hybrid approach that integrates CNNs for image-based data analysis, RNNs for sequential data processing, and attention mechanisms for prioritizing critical threats. This framework is benchmarked against state-of-the-art techniques, demonstrating superior performance in terms of detection accuracy, false-positive rates, and computational efficiency. The research further addresses practical challenges in deploying deep learning in cybersecurity, such as the need for extensive labeled datasets, the risk of model bias, and the computational overhead of real-time threat processing. Strategies for overcoming these challenges are proposed, including the use of transfer learning, data augmentation, and distributed computing. Additionally, ethical considerations and potential risks, such as the dual-use nature of deep learning technologies, are discussed to ensure responsible deployment in cybersecurity contexts. Therefore, this paper underscores the transformative potential of deep learning architectures in modern cybersecurity systems. By automating threat detection and mitigation, these technologies can significantly enhance the security posture of organizations in the face of evolving cyber threats. However, realizing their full potential requires addressing implementation challenges and fostering collaboration between researchers, practitioners, and policymakers. The findings and methodologies presented in this study aim to contribute to the development of robust, scalable, and intelligent cybersecurity solutions that can safeguard critical digital assets in an increasingly interconnected world.

A Phenomenological Inquiry into IT Professionals' Perspectives on AI-Powered Cybersecurity Systems for Intrusion Detection

Cybersecurity has become a critical field in protecting digital infrastructure, with increasing reliance on AI-powered systems for intrusion detection. However, the integration of AI into cybersecurity raises concerns about trust, effectiveness, and the human role in decision-making. Despite significant advancements, there is limited understanding of how IT professionals experience and interact with AI-driven intrusion detection systems in real-world settings. This study aims to explore these experiences and identify factors influencing trust and acceptance of AI in security contexts. Using a phenomenological approach, the research delves into the subjective perspectives of IT professionals, uncovering insights into their interactions with AI systems. Through in-depth interviews and thematic analysis, the findings highlight the dual role of AI as an assistant rather than a complete replacement for human decision-making, while also revealing challenges related to false positives and the need for human oversight. These results contribute to a more nuanced understanding of human-AI collaboration in cybersecurity, emphasizing the ongoing importance of human expertise. The study’s implications suggest that future research should focus on optimizing AI systems for better alignment with user trust and expectations.

Integrating AI, Deep Learning, and Robotics: Transforming Healthcare, Cyber security, and Food Systems for a Sustainable Future

AI, deep learning and robotics are rising as significant shifts across the healthcare, information security, the food industry and supply chain. These technologies facilitate automation, increase effectiveness and effectiveness in decision making process, and therefore tackle global issues such as food insecurity, cyber-crime, and management of natural resources. AI application in farming and food storage increases efficiency and decreases spoilage; AI has a strong aspect of security as it is capable of identifying and analyzing threats within a short span of time. In the more general scope of health care, the uses include diagnostics, endoscopic and robotic surgeries, drug discovery, etc., to help the patients. However, it is crucial that problems like algorithm and data prejudice, privacy, employment losses, restrictions, and rules of ethics should be solving before implementing them. It is significant for the policymakers, industry giant and the researchers to come together to reap the benefits in order to avoid or reduce the risks that are associated with poor implementation of the policy. IT the responsible approach to embedding artificial intelligence allows industries to progress deeply, protect the natural environment, and affect economic development, thus shaping a more liberal, safe, and wise future.

AI-driven cyber threat detection for global logistics in United States

The integration of AI-driven cybersecurity systems is increasingly essential to the security and efficiency of global logistics operations in the United States. This thematic analysis investigates the major themes, challenges, and opportunities of AI in logistics cybersecurity, focusing on its effectiveness in threat detection and prevention, diverse applications, and implementation challenges. AI has been shown to significantly enhance threat detection, reducing response times by up to 65%, and proactively preventing cyber threats through predictive analytics. Key applications include AI-driven network monitoring, endpoint security, and supply chain risk management, each contributing to the overall resilience of logistics networks. However, several challenges hinder the widespread adoption of AI, including high initial costs, integration issues with legacy systems, and a shortage of skilled professionals. Additionally, regulatory compliance and ethical considerations, such as data privacy and transparency, must be addressed to ensure responsible deployment. Despite these barriers, empirical evidence highlights AI’s potential to revolutionize logistics cybersecurity, offering faster threat detection, reduced vulnerabilities, and more secure operations. Overcoming adoption challenges requires a concerted effort from industry stakeholders, with a focus on workforce development, regulatory adherence, and ethical AI practices. Future research should explore strategies to address these challenges, ensuring the full potential of AI in enhancing the security and efficiency of global logistics operations.

Research on Industrial Internet Intrusion Detection Based on Deep Learning Algorithms

With the rapid popularization of the Industrial Internet, the connection between industrial control systems and enterprise networks has become increasingly tight, making them primary targets for cyberattacks. This trend not only intensifies the security risks of industrial control systems but also presents new challenges for cybersecurity protection. Deep learning technology in artificial intelligence, with its capability to learn complex problems from unsupervised data, is a powerful tool for protecting the cybersecurity of industrial control systems. This paper explores the foundational theories of Industrial Internet security and intrusion detection, covering basic concepts, major characteristics, and various security threats and risks faced by the Industrial Internet. It further analyzes the limitations of traditional security technologies in the context of the Industrial Internet, including their inadequacies in responding to new threats, particularly their vulnerability to system vulnerabilities and virus attacks. Moreover, the paper introduces the specific applications of three deep learning algorithmsConvolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs)in the security of the Industrial Internet. Through systematic analysis and research, the paper reveals specific cybersecurity issues present in the Industrial Internet and proposes effective methods to address these issues using deep learning algorithms. These findings not only provide new ideas and technical support for contemporary Industrial Internet security protection but also offer valuable insights and a theoretical foundation for future research directions.

Digital Transformation in Industry 4.0: Legal and Technological Challenges of Cloud Computing and Artificial Intelligence

Industry 4.0 has generated a revolution in production models through the use of advanced technologies such as artificial intelligence (AI) and cloud computing. However, its implementation poses significant legal and technological challenges related to privacy, cybersecurity, intellectual property and interoperability of systems. This article discusses the legal and technological challenges inherent to these technologies in the context of Industry 4.0, based on a literature review of recent research. It also proposes recommendations to address these challenges and promote safe and efficient integration.

Enhanced Grey Wolf Optimization (EGWO) and random forest based mechanism for intrusion detection in IoT networks

Smart devices are enabled via the Internet of Things (IoT) and are connected in an uninterrupted world. These connected devices pose a challenge to cybersecurity systems due attacks in network communications. Such attacks have continued to threaten the operation of systems and end-users. Therefore, Intrusion Detection Systems (IDS) remain one of the most used tools for maintaining such flaws against cyber-attacks. The dynamic and multi-dimensional threat landscape in IoT network increases the challenge of Traditional IDS. The focus of this paper aims to find the key features for developing an IDS that is reliable but also efficient in terms of computation. Therefore, Enhanced Grey Wolf Optimization (EGWO) for Feature Selection (FS) is implemented. The function of EGWO is to remove unnecessary features from datasets used for intrusion detection. To test the new FS technique and decide on an optimal set of features based on the accuracy achieved and the feature taking filters, the most recent FS approach relies on the NF-ToN-IoT dataset. The selected features are evaluated by using the Random Forest (RF) algorithm to combine multiple decision trees and create an accurate result. The experimental outcomes against the most recent procedures demonstrate the capacity of the recommended FS and classification methods to determine attacks in the IDS. Analysis of the results presents that the recommended approach performs more effectively than the other recent techniques with optimized features (i.e., 23 out of 43 features), high accuracy of 99.93% and improved convergence.

EFFECTIVENESS OF E-BANKING FACILITY SERVICES AND CUSTOMER LOYALTY OF USERS OF UNIVERSAL BANK BRANCHES IN DAVAO CITY: BASIS FOR ACTION PLAN

Customer loyalty in e-banking is often influenced by factors such as user-friendly platforms, reliable security measures, and responsive customer support. A lack of focus on these key aspects can lead to customer dissatisfaction, decreased trust, and ultimately, account migration to competitors. Strengthening customer loyalty requires continuously enhancing digital platforms, implementing robust cybersecurity systems, and providing personalized, efficient support to meet evolving customer expectations. These efforts retain existing customers and attract new ones, ensuring long-term growth and success in the competitive e-banking sector. This descriptive-correlational study aimed to determine customer loyalty and e-banking facilities with 240 purposely chosen clients in universal bank branches in Davao City as respondents. Adapted survey questionnaires were used in this study and data were analyzed using descriptive and statistical tools such as Frequency Count, Mean, Standard Deviation, Pearson-r Correlation, ANOVA, and T-test to analyze the study results. Moreover, results revealed that the level of effectiveness of e-banking facilities and customer loyalty is very high. Furthermore, the level of effectiveness of e-banking facilities as rated by universal bank branches is very effective and the level of customer loyalty among universal bank clients is always manifested. KEYWORDS: Business Management, Customer Loyalty, E-Banking, Universal Bank, Correlational, Davao City

Identifying the barriers and enablers of blockchain adoption in Saudi Arabian last-mile logistics using principal component analysis

ABSTRACT Last-mile logistics has emerged as a tool for responsiveness, however, requires dynamic business models and technology to integrate all the stakeholders. Blockchain is emerging as a platform for logistics and supply chain problems due to its prospective features, however, the adoption is slow due to unclear benefits and diverse challenges it brings. This study explores the relationship between last-mile logistics practices (LMLPs), blockchain adoption challenges, blockchain features and blockchain adoption benefits. Data are collected and principal component analysis is used to rank the practices. Finally, we measure the impact of blockchain benefits on LMLPs using regression analysis. The findings revealed that the most important practice is close partnership with customers, followed by a state-of-the-art cybersecurity system. Blockchain features such as transparency, real-time tracking, data-driven decision-making and secure information exchange offer key benefits, including lead-time reduction and enhanced flexibility in supply chains. However, policy uncertainty remains a major challenge to blockchain adoption.

Prioritized Decision Support System for Cybersecurity Selection Based on Extended Symmetrical Linear Diophantine Fuzzy Hamacher Aggregation Operators

The symmetrical linear Diophantine fuzzy Hamacher aggregation operators play a fundamental role in many decision-making applications. The selection of a cyber security system is of paramount importance for maintaining digital assets. It necessitates a comprehensive review of threat landscapes, vulnerability assessments, and the specific needs of the organization in order to ensure the implementation of effective security measures. Smart grid (SG) technology uses modern communication and monitoring technologies to enhance the management and regulation of electricity production and transmission. However, greater dependence on technology and connection creates new vulnerabilities, exposing SG communication networks to large-scale attacks. Unlike previous surveys, which often give broad overviews of SG design, our research goes a step further, giving a full architectural layout that includes major SG components and communication linkages. This in-depth review improves comprehension of possible cyber threats and allows SGs to analyze cyber risks more systematically. To determine the best cybersecurity strategies, this study introduces a multi-criteria group decision-making (MCGDM) approach using the linear Diophantine fuzzy Hamacher prioritized aggregation operator (LDFHPAO). In real-world applications, aggregation operators (AOs) are essential for information fusion. This research presents innovative prioritized AOs designed to address MCGDM problems in uncertain environments. We developed the LDF Hamacher prioritized weighted average (LDFHPWA) and LDF Hamacher prioritized weighted geometric (LDFHPWG) operators, which address the shortcomings of traditional operators and provide a more robust modeling approach for MCGDM challenges. This study also outlines key characteristics of these new prioritized AOs. An MCGDM approach incorporating these operators is proposed and demonstrated to be effective through an example that compares and selects the optimal cybersecurity.

Artificial Intelligence and Deep Learning in Healthcare, Cyber security, and Food Systems: A Comprehensive Review of Applications, Challenges, and Future Directions

The implementation of Artificial Intelligence in healthcare alongside cyber security and food systems produces enhanced efficiency and security functions together with automated processes. Healthcare organizations use AI-powered diagnostic instruments together with predictive data tools and individualized medical approaches to enhance therapeutic results while shortening drug development cycles. AI applications need proper solution to algorithmic biases while protecting data privacy and respecting ethical values for the sake of transparent and fair implementation. Security frameworks which are resilient to emerging exploitative AI techniques together with human oversight have become essential because AI-powered threat detection and automated responses enhance cyber security defenses against cyber-attacks. The upcoming stage of AI advancement will concentrate on three main areas which combine explain ability, trustworthy systems and knowledge transfer between different sectors. Global healthcare advancement along with improved cyber security protection and enhanced food system management are possible through interdisciplinary AI techniques and appropriate challenge management which ensures transparency, security and sustainable practices.

Implementation of Cybersecurity systems in Teleradiology Services- Best Practices

Teleradiology services provides access to healthcare services by allowing radiologists to interpret medical images remotely particularly in rural or underserved regions, while enabling faster diagnostics and treatment. In the last twenty years, the increased internet penetration and digital infrastructure, technological advancements such as Radiology Information System, Picture Archiving and Communication Systems and application of deep learning algorithms in imaging technology has resulted in the significant growth in this field but has also engendered a significant cybersecurity concern. It is important for providers and users to be constantly vigilant and to implement strong information security practices to protect against cyber-threats and to ensure the confidentiality, integrity, and availability of patient data. With cyber threats evolving at an alarming rate, every employee plays a crucial role in protecting an organization from potential attacks. Employees are often the first line of defense, acting as gatekeepers against breaches, phishing scams, and other malicious activities. Therefore, regular employee training is a crucial aspect of the successful implementation of cybersecurity systems. This article provides an overview of the best practices that an ideal cybersecurity system of a teleradiology service provider should follow. An ideal cybersecurity workflow in a teleradiology reporting system should include risk assessment, policy and procedure development, network security through firewalls, data and image encryption, regular employee training, incidence response plan and continuous monitoring and evaluation of the cybersecurity system. A deep understanding of the topic is integral in ensuring the safe, secure and successful implementation of teleradiology in healthcare.

Intelligent Protection: A Study of the Key Drivers of Intention to Adopt Artificial Intelligence (AI) Cybersecurity Systems in the UAE

Aim/Purpose: This research investigates factors influencing consumers’ decisions to use artificial intelligence cybersecurity technology in the United Arab Emirates. Background: The cyber-security risks are getting more complex as technology develops, putting the United Arab Emirates (UAE) businesses and government agencies at risk of severe losses from cybercrime. Methodology: A correlational study design and a quantitative research approach were employed, and 340 professionals working for different government and semi-government organizations in the United Arab Emirates were given questionnaires. The PLS-SEM approach was used to analyze the replies. Contribution: The present research framework remedies the inherent limitations in the PMT model by adding factors used to explain the influence of environmental factors and individual difference factors on behavior. This research framework is an extended application of the PMT model in the context of AI-based cybersecurity systems. Meanwhile, this study confirms the importance of perceived vulnerability in AI technology scenarios. Findings: The findings demonstrated that users’ adoption intentions were significantly and favorably impacted by social influence, facilitating conditions, perceived vulnerability, and perceived response efficacy. Meanwhile, job insecurity enhanced employees’ resistance to change, making resistance to change a major resistance to the intention to adopt AI-based cybersecurity systems. Recommendations for Practitioners: The report offers crucial insights that organizations can utilize to evaluate their readiness for adopting AI-based cybersecurity technologies and create plans to lessen employee resistance to advancements in the cybersecurity industry. Recommendation for Researchers: Researchers on this specific application can make use of the extension of the framework. Impact on Society: The research can be utilized to evaluate their readiness to adopt AI-based cybersecurity technologies. Future Research: Future research should broaden the scope to acquire a more thorough understanding of the behavioral intentions to use AI-cybersecurity systems in the United Arab Emirates. Other elements that could be considered include facilitating settings, Artificial Intelligence knowledge, social impact, effort efficacy, and other frameworks.