Effectively handling imbalanced datasets remains a fundamental challenge in computational modeling and machine learning,particularly when class overlap significantly deteriorates classification performance.Traditional...Effectively handling imbalanced datasets remains a fundamental challenge in computational modeling and machine learning,particularly when class overlap significantly deteriorates classification performance.Traditional oversampling methods often generate synthetic samples without considering density variations,leading to redundant or misleading instances that exacerbate class overlap in high-density regions.To address these limitations,we propose Wasserstein Generative Adversarial Network Variational Density Estimation WGAN-VDE,a computationally efficient density-aware adversarial resampling framework that enhances minority class representation while strategically reducing class overlap.The originality of WGAN-VDE lies in its density-aware sample refinement,ensuring that synthetic samples are positioned in underrepresented regions,thereby improving class distinctiveness.By applying structured feature representation,targeted sample generation,and density-based selection mechanisms strategies,the proposed framework ensures the generation of well-separated and diverse synthetic samples,improving class separability and reducing redundancy.The experimental evaluation on 20 benchmark datasets demonstrates that this approach outperforms 11 state-of-the-art rebalancing techniques,achieving superior results in F1-score,Accuracy,G-Mean,and AUC metrics.These results establish the proposed method as an effective and robust computational approach,suitable for diverse engineering and scientific applications involving imbalanced data classification and computational modeling.展开更多
Cyber threat detection is a crucial aspect of contemporary cybersecurity due to the depth and complexity of cyberattacks.It is the identification of malicious activity,unauthorized access,and possible intrusions in ne...Cyber threat detection is a crucial aspect of contemporary cybersecurity due to the depth and complexity of cyberattacks.It is the identification of malicious activity,unauthorized access,and possible intrusions in networks and systems.Modern detection methods employ artificial intelligence and machine learning to study vast amounts of data,learn patterns,and anticipate potential threats.Real-time monitoring and anomaly detection improve the capacity to react to changing threats more rapidly.Cyber threat detection systems aim to reduce false positives and provide complete coverage against the broadest possible attacks.This research advocates for proactive measures and adaptive technologies in defending digital environments.Improvements in detection ability by organizations will assist in safeguarding assets and integrity in operations in this increasingly digital world.This paper draws on the categorization of cyber threat detection methods using hesitant bipolar fuzzy Frank operators.Categorization is a step that is necessary for systematic comparison and assessment of detection methods so that the most suitable method for particular cybersecurity requirements is chosen.Furthermore,this research manages uncertainty and vagueness that exists in decision-making by applying hesitant bipolar fuzzy logic.The importance of the work lies in how it fortifies cybersecurity architectures with a formal method of discovering optimal detection measures and improving responsiveness,resulting in holistic protection against dynamic threats.展开更多
The evaluation and assessment of network security is a decision-making(DM)problem that occurs in an environment with multiple criteria,which have uncertainty,bipolarity,and extra-related information.The traditional ap...The evaluation and assessment of network security is a decision-making(DM)problem that occurs in an environment with multiple criteria,which have uncertainty,bipolarity,and extra-related information.The traditional approaches fail to address the need to acquire a wide range of information for the assessment,especially in situations where the criteria have both positive and negative aspects and contain extra fuzzy information.Therefore,in this manuscript,we aim to introduce a DM approach based on the concept of bipolar complex fuzzy(BCF)Yager aggregation operators(AOs).The related properties of these aggregation operators(AOs)are also discussed.Moreover,in this article,we diagnose the Yager operations in the setting of BCF.The basic idea of the interpreted operators and DM approach is to access the problem linked with the network security that is to evaluate and select the finest network security control and network security protocols for protecting and safeguarding the network of any organization or home(case studies).Finally,to exhibit the supremacy and success of the described theory,we examine them with the prevailing theories.展开更多
基金supported by Ongoing Research Funding Program(ORF-2025-488)King Saud University,Riyadh,Saudi Arabia.
文摘Effectively handling imbalanced datasets remains a fundamental challenge in computational modeling and machine learning,particularly when class overlap significantly deteriorates classification performance.Traditional oversampling methods often generate synthetic samples without considering density variations,leading to redundant or misleading instances that exacerbate class overlap in high-density regions.To address these limitations,we propose Wasserstein Generative Adversarial Network Variational Density Estimation WGAN-VDE,a computationally efficient density-aware adversarial resampling framework that enhances minority class representation while strategically reducing class overlap.The originality of WGAN-VDE lies in its density-aware sample refinement,ensuring that synthetic samples are positioned in underrepresented regions,thereby improving class distinctiveness.By applying structured feature representation,targeted sample generation,and density-based selection mechanisms strategies,the proposed framework ensures the generation of well-separated and diverse synthetic samples,improving class separability and reducing redundancy.The experimental evaluation on 20 benchmark datasets demonstrates that this approach outperforms 11 state-of-the-art rebalancing techniques,achieving superior results in F1-score,Accuracy,G-Mean,and AUC metrics.These results establish the proposed method as an effective and robust computational approach,suitable for diverse engineering and scientific applications involving imbalanced data classification and computational modeling.
基金funded by Ongoing Research Funding program(ORF-2025-749),King Saud University,Riyadh,Saudi Arabia.
文摘Cyber threat detection is a crucial aspect of contemporary cybersecurity due to the depth and complexity of cyberattacks.It is the identification of malicious activity,unauthorized access,and possible intrusions in networks and systems.Modern detection methods employ artificial intelligence and machine learning to study vast amounts of data,learn patterns,and anticipate potential threats.Real-time monitoring and anomaly detection improve the capacity to react to changing threats more rapidly.Cyber threat detection systems aim to reduce false positives and provide complete coverage against the broadest possible attacks.This research advocates for proactive measures and adaptive technologies in defending digital environments.Improvements in detection ability by organizations will assist in safeguarding assets and integrity in operations in this increasingly digital world.This paper draws on the categorization of cyber threat detection methods using hesitant bipolar fuzzy Frank operators.Categorization is a step that is necessary for systematic comparison and assessment of detection methods so that the most suitable method for particular cybersecurity requirements is chosen.Furthermore,this research manages uncertainty and vagueness that exists in decision-making by applying hesitant bipolar fuzzy logic.The importance of the work lies in how it fortifies cybersecurity architectures with a formal method of discovering optimal detection measures and improving responsiveness,resulting in holistic protection against dynamic threats.
基金funded by Ongoing Research Funding Program(Grant ORF‐2025-749),King Saud University,Riyadh,Saudi Arabia.
文摘The evaluation and assessment of network security is a decision-making(DM)problem that occurs in an environment with multiple criteria,which have uncertainty,bipolarity,and extra-related information.The traditional approaches fail to address the need to acquire a wide range of information for the assessment,especially in situations where the criteria have both positive and negative aspects and contain extra fuzzy information.Therefore,in this manuscript,we aim to introduce a DM approach based on the concept of bipolar complex fuzzy(BCF)Yager aggregation operators(AOs).The related properties of these aggregation operators(AOs)are also discussed.Moreover,in this article,we diagnose the Yager operations in the setting of BCF.The basic idea of the interpreted operators and DM approach is to access the problem linked with the network security that is to evaluate and select the finest network security control and network security protocols for protecting and safeguarding the network of any organization or home(case studies).Finally,to exhibit the supremacy and success of the described theory,we examine them with the prevailing theories.
