Intelligent classification of computer vulnerabilities and network security management system: Combining memristor neural network and improved TCNN model.

To enhance the intelligent classification of computer vulnerabilities and improve the efficiency and accuracy of network security management, this study delves into the application of a comprehensive classification system that integrates the Memristor Neural Network (MNN) and an improved Temporal Co...

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Main Author: Zhenhui Liu
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2025-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0318075
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author Zhenhui Liu
author_facet Zhenhui Liu
author_sort Zhenhui Liu
collection DOAJ
description To enhance the intelligent classification of computer vulnerabilities and improve the efficiency and accuracy of network security management, this study delves into the application of a comprehensive classification system that integrates the Memristor Neural Network (MNN) and an improved Temporal Convolutional Neural Network (TCNN) in network security management. This system not only focuses on the precise classification of vulnerability data but also emphasizes its core role in strengthening the network security management framework. Firstly, the study designs and implements a neural network model based on memristors. The MNN, by simulating the memory effect of biological neurons, effectively captures the complex nonlinear relationships within vulnerability data, thereby enhancing the data insight capabilities of the network security management system. Subsequently, structural optimization and parameter adjustments are made to the TCNN model, incorporating residual connections and attention mechanisms to improve its classification performance, making it more adaptable to the dynamically changing network security environment. Through data preprocessing, feature extraction, and model training, this study conducts experimental validation on a public vulnerability dataset. The experimental results indicate that: The MNN model demonstrates excellent performance across evaluation metrics such as Accuracy (ACC), Precision (P), Recall (R), and F1 Score, achieving an ACC of 89.5%, P of 90.2%, R of 88.7%, and F1 of 89.4%. The improved TCNN model shows even more outstanding performance on the aforementioned evaluation metrics. After structural optimization and parameter adjustments, the TCNN model's ACC increases to 93.8%, significantly higher than the MNN model. The P value also improves, reaching 91.5%, indicating enhanced capability in reducing false positives and improving vulnerability identification accuracy. The integrated classification system, leveraging the strengths of both the MNN and improved TCNN models, achieves an ACC of 95.2%. This improvement not only demonstrates the system's superior capability in accurately classifying vulnerability data but also proves the synergistic effect of MNN and TCNN models in addressing complex network security environments. The comprehensive classification system proposed in this study significantly enhances the classification performance of computer vulnerabilities, providing robust technical support for network security management. The system exhibits higher accuracy and stability in handling complex vulnerability datasets, making it highly valuable for practical applications and research.
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spelling doaj-art-093103e51ba5447688cfa35bf70f95332025-02-05T05:32:04ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031807510.1371/journal.pone.0318075Intelligent classification of computer vulnerabilities and network security management system: Combining memristor neural network and improved TCNN model.Zhenhui LiuTo enhance the intelligent classification of computer vulnerabilities and improve the efficiency and accuracy of network security management, this study delves into the application of a comprehensive classification system that integrates the Memristor Neural Network (MNN) and an improved Temporal Convolutional Neural Network (TCNN) in network security management. This system not only focuses on the precise classification of vulnerability data but also emphasizes its core role in strengthening the network security management framework. Firstly, the study designs and implements a neural network model based on memristors. The MNN, by simulating the memory effect of biological neurons, effectively captures the complex nonlinear relationships within vulnerability data, thereby enhancing the data insight capabilities of the network security management system. Subsequently, structural optimization and parameter adjustments are made to the TCNN model, incorporating residual connections and attention mechanisms to improve its classification performance, making it more adaptable to the dynamically changing network security environment. Through data preprocessing, feature extraction, and model training, this study conducts experimental validation on a public vulnerability dataset. The experimental results indicate that: The MNN model demonstrates excellent performance across evaluation metrics such as Accuracy (ACC), Precision (P), Recall (R), and F1 Score, achieving an ACC of 89.5%, P of 90.2%, R of 88.7%, and F1 of 89.4%. The improved TCNN model shows even more outstanding performance on the aforementioned evaluation metrics. After structural optimization and parameter adjustments, the TCNN model's ACC increases to 93.8%, significantly higher than the MNN model. The P value also improves, reaching 91.5%, indicating enhanced capability in reducing false positives and improving vulnerability identification accuracy. The integrated classification system, leveraging the strengths of both the MNN and improved TCNN models, achieves an ACC of 95.2%. This improvement not only demonstrates the system's superior capability in accurately classifying vulnerability data but also proves the synergistic effect of MNN and TCNN models in addressing complex network security environments. The comprehensive classification system proposed in this study significantly enhances the classification performance of computer vulnerabilities, providing robust technical support for network security management. The system exhibits higher accuracy and stability in handling complex vulnerability datasets, making it highly valuable for practical applications and research.https://doi.org/10.1371/journal.pone.0318075
spellingShingle Zhenhui Liu
Intelligent classification of computer vulnerabilities and network security management system: Combining memristor neural network and improved TCNN model.
PLoS ONE
title Intelligent classification of computer vulnerabilities and network security management system: Combining memristor neural network and improved TCNN model.
title_full Intelligent classification of computer vulnerabilities and network security management system: Combining memristor neural network and improved TCNN model.
title_fullStr Intelligent classification of computer vulnerabilities and network security management system: Combining memristor neural network and improved TCNN model.
title_full_unstemmed Intelligent classification of computer vulnerabilities and network security management system: Combining memristor neural network and improved TCNN model.
title_short Intelligent classification of computer vulnerabilities and network security management system: Combining memristor neural network and improved TCNN model.
title_sort intelligent classification of computer vulnerabilities and network security management system combining memristor neural network and improved tcnn model
url https://doi.org/10.1371/journal.pone.0318075
work_keys_str_mv AT zhenhuiliu intelligentclassificationofcomputervulnerabilitiesandnetworksecuritymanagementsystemcombiningmemristorneuralnetworkandimprovedtcnnmodel