Enhancing Cyber Security Research Articles

A collaborative cybersecurity framework for higher education

Purpose The purpose of this paper is to present the educational computer emergency response team (EduCERT) framework, an integrated response mechanism to bolster national cybersecurity through collaborative efforts in the higher education sector. The EduCERT framework addresses this gap by enhancing cyber security and mitigating cybercrime through collaborative incident management, knowledge sharing and university awareness campaigns. Design/methodology/approach The authors propose an EduCERT framework following the design science methodology. The framework is developed based on literature and input from focus group experts. Moreover, it is grounded in the principles of the technology-organization-environment framework, organizational learning and diffusion of innovations theory. Findings The EduCERT has eight components: infrastructure, governance, knowledge development, awareness, incident management, evaluation and continuous improvement. The framework reinforces national cybersecurity through cooperation between universities and the National Computer Emergency Response Team. The framework has been implemented in Jordan to generate a cybersecurity foundation for higher education. Evaluating the EduCERT framework’s influence on national cybersecurity highlights the importance of adopting comprehensive cyber-security policies and controls. The framework application shows its relevance, effectiveness, adaptability and alignment with best practices. Research limitations/implications Despite the impact of applying the framework in the Jordanian context, it is essential to acknowledge that the proposed EduCERT framework’s practical implementation may encounter challenges specific to diverse international educational environment sectors. However, framework customization for global applicability could address varied educational institutions in other countries. Practical implications Furthermore, the proposed EduCERT framework is designed with universal applicability that extends beyond the specific country’s context. The principles and components presented in the framework can serve as valuable design advice for establishing collaborative and resilient cybersecurity frameworks in educational settings worldwide. Therefore, the research enhances the proposed framework’s practical utility and positions it as an invaluable contribution to the broader discourse on global cybersecurity in academia. Originality/value This paper enhances national cybersecurity in the higher education sector, addressing the need for a more integrated response mechanism. The EduCERT framework demonstrates its effectiveness, adaptability and alignment with best practices, offering valuable guidance for global educational institutions.

Optimization of machine learning model training procedure on multi-gpu systems to enhance cyber security in telecommunication networks

This paper analyzes the optimization features of machine learning (ML) model training procedures using multi-GPU systems to enhance cyber security in telecommunication networks. A key aspect of the study is the use of data parallelism, which allows the distribution of the training load across multiple GPUs, significantly reducing training time and improving model accuracy-critical factors for rapid threat detection in cyberspace. A novel approach for optimizing data batch size using Mutual Information (MI) is proposed, which harmonizes the utilization of computational resources with the information content of the training data. MI helps to determine the optimal data batch size that minimizes training errors and improves model accuracy without a significant increase in training time. Experimental results demonstrate the substantial advantages of multi-GPU configurations compared to single-GPU setups, providing faster training and improved model accuracy. It was particularly emphasized that MI-guided batch size tuning significantly outperforms traditional manual tuning methods, ensuring higher validation accuracy and reducing training time. The study showed that the MI-based approach is an effective tool for addressing the problem of optimizing ML model training processes in real-world scenarios where cyber security is critical. The proposed methods allow ML models to train faster and more accurately identify potential threats, making them particularly relevant for telecommunication networks where a rapid response to new threats in real time is required. The implementation of modern computational technologies such as multi-GPU systems and MI-optimized training enhances the efficiency and accuracy of machine learning models. This, in turn, improves cyber security measures and ensures a more reliable defence of telecommunication networks against malicious attacks. It is noted that the proposed approaches can be adapted not only for cyber security but also for other areas where high model accuracy and fast training are important. Future research prospects include the development of new machine learning methods, particularly deep neural networks, the exploration of alternative computational architectures such as quantum computing or distributed systems, and their integration into real-time systems. Special attention should be paid to the ethical aspects of implementing automated cyber security systems, particularly in preventing bias in algorithms and ensuring fairness in their application.

THE IMPACT OF THE USE OF INFORMATION TECHNOLOGY ON THE ACCOUNTING PROFESSION IN THE ACCOUNTING LABOR MARKET IN THE KURDISTAN REGION, A FIELD STUDY OF PROFESSIONALS IN COMPANIES AND ACADEMICS IN ACCOUNTING DEPARTMENTS IN ERBIL GOVERNORATE

This study aims to explore the impact of the use of information technology on the accounting profession in the labor market in the Kurdistan Region, with a special focus on the opinions of professionals working in companies and academics in accounting departments in Erbil Governorate. The main problem addressed by the study is based on the challenge that accounting education faces in keeping pace with rapid developments in information technology and integrating them effectively within educational programs. In light of the significant expansion of the use of technology in various areas of life, including accounting, the urgent need for university accounting departments to provide the necessary scientific and practical skills to students is highlighted, to qualify them to work in an advanced technological accounting environment. Accordingly, the basic hypothesis of the study was formulated: “Using technology Information in accounting education raises the academic and professional competence of students in accounting departments, enabling them to keep pace with the requirements of the contemporary labor market in the Kurdistan region. To achieve the objectives of the study and examine the hypothesis, the descriptive analytical approach was adopted, which is suitable for studying the relationship between the use of information technology in accounting education and the competence of graduates. The descriptive aspect includes the use of relevant scientific literature and references, while in the analytical aspect, data is collected through questionnaires directed to a sample of the study, and analyzed using the Statistical Package for the Social Sciences (SPSS) program. The results highlight the importance of information technology in accounting and confirm the existence of a relationship There is a strong connection between the technological qualification of accountants and the actual reality of their profession in the region. Therefore, the study recommends the need to pay attention to continuously evaluating accounting educational programs and developing them to meet the changing needs of the labor market, including providing specialized training courses in information technology, modernizing the technological infrastructure, and enhancing cyber security for data. Accounting. These measures will contribute to improving the competence and skills of accounting graduates tobetter prepare them to face contemporary challenges in the accounting profession.

Enhancing Cybersecurity Using Artificial Intelligence: An Analysis of Neural Network and Machine Learning Algorithms"

Enhancing Cybersecurity Using Artificial Intelligence: An Analysis of Neural Network and Machine Learning Algorithms"

Enhancing Cybersecurity with Blockchain-based Identity Management

In the digital age, the rapid increase in cyber threats has highlighted the need for more robust and secure identity management systems. Traditional centralized identity management systems are often vulnerable to data breaches, unauthorized access, and identity theft, posing significant risks to individuals and organizations. This paper explores the potential of blockchain technology to enhance cybersecurity through decentralized identity management. By leveraging the inherent properties of blockchain—such as immutability, transparency, and decentralization—blockchain-based identity management systems offer a more secure and efficient alternative to traditional methods. The paper provides an in-depth analysis of how blockchain can be utilized to store and manage digital identities securely, reducing the risk of identity fraud and data breaches. It examines various blockchain-based identity management frameworks, highlighting their advantages and potential challenges in implementation. Furthermore, the study discusses the integration of blockchain with other emerging technologies, such as cryptographic algorithms and smart contracts, to create a more resilient cybersecurity infrastructure. Through a review of current literature and case studies, the paper demonstrates the effectiveness of blockchain in enhancing data security and privacy while promoting user control over personal information. The findings suggest that adopting blockchain-based identity management could significantly mitigate cybersecurity threats and enhance trust in digital transactions. However, the paper also acknowledges the need for addressing scalability, regulatory compliance, and interoperability issues to fully realize the potential of blockchain in cybersecurity. This research contributes to the growing body of knowledge on innovative cybersecurity solutions and provides a foundation for future studies in the field.

Enhancing cybersecurity in cloud computing and WSNs: A hybrid IDS approach

The evolution of cloud computing has revolutionized how users access services, simplifying the development and deployment of applications across various industries. With its pervasive adoption, robust security measures become imperative. Integrating Intrusion Detection Systems (IDSs) into cloud computing and Wireless Sensor Networks (WSNs) addresses these challenges. IDSs serve as attentive protectors, monitoring network traffic and responding to breaches promptly, enhancing security across industries reliant on cloud services. Similarly, IDS integration in WSNs ensures the security of mission-critical operations, despite resource constraints and dynamic topologies, facilitated by cloud computing. This research proposes a hybrid IDS approach, leveraging the NSL-KDD dataset and methodologies like Intrusion Support Scalar Impact Rate (ISSIR), Optimized Support Vector Machine (OSVM), Extended Long-Short-Term Memory (ELSTM), and Multilayer Perceptron Neural Network (MLPNN), enhancing intrusion detection efficacy. ISSIR aids in feature selection, OSVM mitigates localization errors, ELSTM enables precise anomaly detection, and MLPNN provides robust defense mechanisms. Each method is integrated into a collaborative framework to address specific challenges in detecting intrusions with higher accuracy and reduced false positives. The interplay between these methodologies strengthens the overall intrusion detection framework, addressing the dynamic nature of cybersecurity threats. Results demonstrate the superior performance of MLPNN across various metrics, showcasing its effectiveness in accurately predicting outcomes compared to other models. The proposed MLPNN hybrid system achieves an accuracy of 99.9%, surpassing state-of-the-art methods. This study underscores the significance of advancing IDSs in cloud computing and WSNs, offering insights into enhancing security and mitigating vulnerabilities in an interconnected digital landscape.

Enhancing Cybersecurity through the Integration of Geographic Information Systems Technologies

Enhancing Cybersecurity through the Integration of Geographic Information Systems Technologies

Enhancing Cybersecurity in Onboard Charging Systems of Electric Vehicles: A MATLAB-based Approach

This research provides a comprehensive analysis of the impacts of various data integrity attacks on the power electronic hardware of electric vehicle (EV) chargers. The study aims to offer essential recommendations for defending against cyberattacks on electric vehicles and their onboard charging (OBC) systems. Adverse scenarios resulting from such cyberattacks are meticulously examined, and the system is simulated in MATLAB/Simulink to validate fault occurrences under different data integrity attacks. The quantitative analysis of the results clearly indicates that by incorporating adequate precautionary measures, such as integrating intelligent controls into the main controller during the design of the charging architecture, it is possible to significantly mitigate electrical hazards and prevent the degradation of component health, even in the event of a malicious cyberattack.

Enhancing cyber security: a comprehensive approach to the classification and prediction of significant cyber incidents (SCI) through data mining and variational neural network with fox optimization algorithm

The rapid advancement of technology and the proliferation of IoT devices have rendered cyberspace vulnerable, resulting in Significant Cyber Incidents (SCIs). This paper proposes an Enhancing Cyber security: a Comprehensive Approach to the Classification with Prediction of Significant Cyber Incidents through Data Mining with Variation Neural Network with Fox Optimization Algorithm (ECS-SCI-DM-VNN-FOA). The dataset is split into pre-pandemic and post-pandemic SCI subsets, according to the report from the Center for Strategic and International Studies (CSIS). Adaptive Variation Bayesian Filter (AVBF) is utilized to remove the noise from the input data. Then the preprocessed input data is supplied to the Improved Window Adaptive Gray Level Co-Occurrence Matrix (IWAGM) for feature extraction. The proposed approach is implemented in Python and its efficacy is evaluated under some metrics, like accuracy, precision, recall, FI-score, sensitivity, computational time, recall, and RoC. The proposed ECS-SCI-DM-VNN-FOA gives 24.91%, 23.76% and 25.98% high accuracy and 30.45%, 23.67% and 29.32% high precision compared with the existing methods, like classification with prediction of cyber events utilizing data mining with machine learning (CRP-SML), Cyber risk prediction via social media big data analytics along statistical machine learning (PECI-MUIP), Mining user interaction patterns in the dark web to forecast enterprise cyber occurrences (CTPA-CSCS) respectively.

Enhancing Cybersecurity Against DDOS Attacks Evaluating Supervised Machine Learning Techniques

An individual or group launches a cyber attack when they intentionally try to get into another person's or group's computer system. Typically, the goal of an attacker is to gain an advantage by interfering with the victim's network. Now that COVID-19 has wreaked havoc on businesses throughout the world, it's cybercriminals' ideal storm. When it comes to cyber threats, Distributed Denial-Of-Service attacks (DDoS) are the most common and dangerous for corporate networks, apps, and services. Distributed denial of service attacks aim to flood a server, service, or network with malicious traffic in an effort to interrupt regular traffic. Financial losses, decreased productivity, damaged brands, worse credit and insurance ratings, damaged relationships with suppliers and customers, and IT budget overruns are all possible outcomes. Developing Network Intrusion Detection Systems (NIDSs) that can reliably foretell DDoS attacks is an urgent issue. This study used the CICDDoS2019 dataset to assess supervised Machine Learning (ML) methods. The machine learning algorithms that were assessed include AdaBoost, Naïve Bayes, MLP-ANN, Random Forest, and SVM. We use the assessment metrics: Area Under the Curve (AUC), Accuracy, F-measure, Precision, and Recall. This study demonstrates that of the algorithms tested, AdaBoost shows the highest promise in detecting DDoS attacks

LSTM Based New Probability Features Using Machine Learning to Improve Network Attack Detection

This project focuses on improving the detection of network attacks by using a machine learning technique known as Long Short-Term Memory (LSTM) networks. LSTM networks are a type of neural network that excels at analyzing sequences of data, making them well-suited for identifying patterns associated with network intrusions. To enhance the LSTM model's effectiveness, we introduce new probability features that help the model better distinguish between normal and malicious activities. Our approach includes collecting network data, preprocessing it to make it suitable for training, and then using this data to train the LSTM model. We evaluate the model's performance using a range of metrics to ensure its accuracy and reliability. The results indicate that our method significantly improves the detection rate of network attacks while also reducing the number of false alarms. This means that our LSTM-based model not only catches more real threats but also makes fewer mistakes in identifying normal activities as attacks. Overall, this project showcases the potential of advanced machine learning techniques, like LSTM networks, to enhance cyber security measures and protect against network threats more effectively.

Enhancing Cybersecurity through Machine Learning: An Exploration of Anomaly Detection

In the contemporary digital environment, cybersecurity is one of the most crucial areas to take care of. The rising sophistication of cyber threats poses a severe risk to individuals and businesses. Below is the research work of elaboration on the application of machine learning techniques in the improved anomaly detection for cybersecurity. The study will detect and attempt to mitigate more anomalous activities indicating possible cyber threats using Machine Learning algorithms. More concretely, this study consists of a thorough literature review of existing works on cybersecurity and machine learning, delves into a variety of algorithms for anomaly detection, and evaluates their empirical performance.

HoneyPot : Enhancing Cybersecurity through a computer simulation technology

HoneyPot : Enhancing Cybersecurity through a computer simulation technology

Enhancing cybersecurity protocols in the era of big data and advanced analytics

In the modern digital landscape, the exponential growth of big data and the proliferation of advanced analytics present both unprecedented opportunities and significant challenges for cybersecurity. This review explores the imperative of enhancing cybersecurity protocols to safeguard sensitive information and ensure the integrity of digital infrastructures in an era characterized by vast data generation and sophisticated analytical techniques. As organizations across various sectors leverage big data to drive innovation and gain competitive advantages, they simultaneously face heightened risks from cyber threats. Advanced analytics, including machine learning and artificial intelligence, offer potent tools for detecting and mitigating these threats. However, the integration of such technologies into cybersecurity frameworks demands a comprehensive and forward-thinking approach. Key to this enhancement is the development of robust data governance policies that ensure data integrity, confidentiality, and availability. These policies must address the complexities introduced by diverse data sources, varied data formats, and the velocity at which data is generated and processed. Additionally, the implementation of machine learning algorithms can significantly improve threat detection capabilities by identifying patterns and anomalies indicative of cyber threats, thus enabling proactive defense mechanisms. Moreover, enhancing cybersecurity protocols involves the adoption of encryption techniques and secure communication channels to protect data both at rest and in transit. Continuous monitoring and real-time analytics are crucial for maintaining situational awareness and promptly responding to potential breaches. The utilization of big data analytics also facilitates the identification of vulnerabilities and the assessment of risk profiles, allowing for the prioritization of security measures based on threat severity and impact. Despite the technological advancements, challenges such as data privacy concerns, algorithmic biases, and the need for skilled cybersecurity professionals persist. Addressing these challenges requires a multi-faceted strategy encompassing regulatory compliance, ethical considerations, and ongoing education and training. In conclusion, enhancing cybersecurity protocols in the era of big data and advanced analytics is essential for protecting critical digital assets and maintaining trust in digital ecosystems. By integrating cutting-edge analytical tools and establishing comprehensive data governance frameworks, organizations can effectively mitigate cyber risks and leverage the full potential of big data for sustainable growth and innovation.

Leveraging Artificial Intelligence for Enhancing Cybersecurity: A Comprehensive Review and Analysis

Leveraging Artificial Intelligence for Enhancing Cybersecurity: A Comprehensive Review and Analysis

Enhancing Cybersecurity through GAN-Augmented and Hybrid Feature Selection Machine Learning Models: A Case Study on EVSE Data

Enhancing Cybersecurity through GAN-Augmented and Hybrid Feature Selection Machine Learning Models: A Case Study on EVSE Data

Enhancing Cybersecurity Posture through Dynamic Vulnerability Matching and Threat Intelligence Integration Precious

As the digital landscape continues to evolve, organizations face increasingly sophisticated cyber threats that challenge traditional cybersecurity measures. In response, this paper proposes a novel approach to bolstering cybersecurity posture by integrating dynamic vulnerability matching and threat intelligence. The proposed framework combines proactive identification of vulnerabilities within an organization's network with real-time threat intelligence feeds. Leveraging advanced analytics and machine learning algorithms, the system dynamically matches vulnerabilities to relevant threat intelligence, allowing for prioritized remediation efforts. This dynamic matching ensures that resources are allocated efficiently, focusing on mitigating the most imminent threats to the organization's security. Furthermore, the integration of threat intelligence enriches the vulnerability management process by providing contextual information about emerging threats, attack vectors, and adversary tactics. This contextual awareness enables organizations to anticipate and proactively defend against potential cyber attacks, thereby reducing the window of vulnerability and minimizing the impact of security breaches. Through empirical evaluation and case studies, we demonstrate the efficacy of the proposed framework in enhancing cybersecurity posture across diverse organizational environments. By empowering organizations to adaptively respond to evolving cyber threats, this approach enables them to stay ahead of adversaries and effectively safeguard their critical assets and data.

Enhancing Cyber Security Using Quantum Computing and Artificial Intelligence: A Review

This article examines the transformative potential of quantum computing in addressing the growing challenge of cyber threats. With traditional encryption methods becoming increasingly ineffective against sophisticated cyber attacks, quantum computing emerges as a promising solution, offering unparalleled computational capabilities for enhancing cyber security. This technology is poised to revolutionize how we protect sensitive data by developing quantum-resistant encryption algorithms and quantum-based machine learning modules to safeguard critical infrastructures. By exploring the intersection between quantum computing and cyber security, this article highlights the opportunities, challenges, and prospects of leveraging quantum advancements to strengthen our defenses against the evolving landscape of cyber threats

Enhancing cybersecurity in Edge IIoT networks: An asynchronous federated learning approach with a deep hybrid detection model

In the rapidly evolving field of the Industrial Internet of Things (IIoT), advancements in wireless technology have resulted in significant cybersecurity vulnerabilities. These weaknesses pose serious risks such as damage to manufacturing systems, theft of intellectual property, and substantial financial losses. This study introduces an advanced deep hybrid learning model in an asynchronous federated learning setup, aimed at improving the detection of cyberattacks and ensuring robust data privacy. The combination of Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM) networks provides an effective solution for quickly identifying anomalies in IIoT sensor traffic. Our model operates asynchronously, ensuring data remains localised to improve security while avoiding the need for complete node synchronisation. Demonstrating outstanding effectiveness, the model achieves an accuracy of 1.00%, precision of 1.00%, recall of 1.00%, and an F1 score of 1.00% across a variety of IIoT environments. These results highlight the model’s exceptional adaptability and its capability to rapidly respond to emergent threats, marking a significant step forward in the protection of IIoT infrastructures and the rigorous maintenance of data privacy.

Assessing the Vulnerabilities: Cybersecurity Challenges in Power System Infrastructure in Nigeria

This study examined the Cyber security Challenges in Power System Infrastructure in Nigeria. Four research questions guided the study which employed a survey research design. The population of the study was 46 respondents comprising 23 Technical Units Heads and 23 Heads of Cyber security Units from the (23) power-generating plants in Nigeria. The entire population was used hence, there was no sampling. The instrument used for data collection was a structured questionnaire titled: Cyber security Challenges in Power System Infrastructure Questionnaire (CCPSIQ) developed by the researchers. A reliability index of 0.89 was obtained using the Cronbach Alpha reliability method. The mean statistic was used to answer the research questions. The findings of the study revealed that the primary cyber threats targeting power system infrastructure in Nigeria including network-based attacks, insider threats, supply chain vulnerabilities, advanced persistent threats (APTs), physical attacks, zero-day exploits, data breaches, and ransom ware/extortion; it further reveal that cyber-attacks on power grid infrastructure in Nigeria lead to significant service disruptions, financial losses, equipment damage, safety compromises, erosion of customer confidence, regulatory non-compliance, increased vulnerability to future incidents, reputation damage, and prolonged recovery periods, underscoring the multifaceted impact of cyber threats on operational efficiency. The study recommended that: Collaboration among stakeholders, including government agencies, industry partners, and cyber security experts, is paramount to effectively address emerging threats and enhance cyber security resilience. By implementing proactive measures and fostering a culture of cyber security awareness, Nigeria can strengthen its power system infrastructure against cyber threats, safeguarding the reliability and security of critical services for its citizens.