A BIPMU-based network security situation assessment method for wireless network

Liu, Zi-yi; Yang, Changsong; Liu, Yueling; Ding, Y.

doi:10.1016/j.csi.2022.103661

Cited by 10 publications

(5 citation statements)

References 28 publications

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“…However, the model needs further refinement to improve its applicability and efficiency for practical applications. In 2023, Ziyi Liu et al [29] developed a wireless situation assessment approach utilizing the bidirectional parallel memory unit (BIPMU) to effectively evaluate real-time security conditions within wireless networks. Experimental outcomes underscore the heightened efficiency and accuracy of the proposed method when contrasted with earlier approaches.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Internet of Things Security Situation Assessment Model with Feature Optimization and Improved SSA-LightGBM

Xie,

Li,

et al. 2023

Mathematics

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In this paper, an improved Internet of Things (IoT) network security situation assessment model is designed to solve the problems arising from the existing IoT network security situation assessment approach regarding feature extraction, validity, and accuracy. Firstly, raw data are dimensionally reduced using independent component analysis (ICA), and the weights of all features are calculated and fused using the maximum relevance minimum redundancy (mRMR) algorithm, Spearman’s rank correlation coefficient, and extreme gradient boosting (XGBoost) feature importance method to filter out the optimal subset of features. Piecewise chaotic mapping and firefly perturbation strategies are then used to optimize the sparrow search algorithm (SSA) to achieve fast convergence and prevent getting trapped in local optima, and then the optimized algorithm is used to improve the light gradient boosting machine (LightGBM) algorithm. Finally, the improved LightGBM method is used for training to calculate situation values based on a threat impact to assess the IoT network security situation. The research findings reveal that the model attained an evaluation accuracy of 99.34%, sustained a mean square error at the 0.00001 level, and reached its optimum convergence value by the 45th iteration with the fastest convergence speed. This enables the model to more effectively evaluate the IoT network security status.

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Section: Related Workmentioning

confidence: 99%

Enhanced Internet of Things Security Situation Assessment Model with Feature Optimization and Improved SSA-LightGBM

Xie,

Li,

et al. 2023

Mathematics

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“…Liu et al [10] studied the characteristics of wireless networks and proposed an NSSA method based on BIPMU to improve the performance and accuracy of NSSA. Zhao et al [11] analyzed NSSA in the big data environment, selected multi-source data in the big data environment, proposed a parallel reduction algorithm based on an attribute importance matrix to reduce the number of attributes of data sources, and used the particle swarm optimization algorithm to calculate the situation value of a wavelet neural network.…”

Section: Related Workmentioning

confidence: 99%

AHP-Based Network Security Situation Assessment for Industrial Internet of Things

Guo

2023

Electronics

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The Industrial Internet of Things (IIoT) is used in various industries to achieve industrial automation and intelligence. Therefore, it is important to assess the network security situation of the IIoT. The existing network situation assessment methods do not take into account the particularity of the IIoT’s network security requirements and cannot achieve accurate assessment. In addition, IIoT transmits a lot of heterogeneous data, which is subject to cyber attacks, and existing classification methods cannot effectively deal with unbalanced data. To solve the above problems, this paper first considers the special network security requirements of the IIoT, and proposes a quantitative evaluation method of network security based on the Analytic Hierarchy Process (AHP). Then, the average under-/oversampling (AUOS) method is proposed to solve the problem of unbalance of network attack data. Finally, an IIoT network security situation assessment classifier based on the eXtreme Gradient Boosting (XGBoost) is constructed. Experiments show that the situation assessment method proposed in this paper can more accurately characterize the network security state of the IIoT. The AUOS method can achieve data balance without generating too much data, and does not burden the training of the model. The classifier constructed in this paper is superior to the traditional classification algorithm.

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“…BiPMU ( Liu et al, 2023 ): Using bidirectional PMU (BiPMU) to evaluate the network security situation value.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…Consequently, the accuracy of network security situation assessment is not particularly high. Deep neural networks (DNNs) present a potential solution to these issues due to their strong ability to represent high-dimensional complex data ( Liu et al, 2023 ). Most existing works on network security situation assessment primarily focus on current traffic features, which are more informative ( Gao et al, 2015 ; Liu et al, 2017 ; Shi & Chen, 2017 ; Chen, Yin & Sun, 2018 ; Qiang, Wang & Dang, 2018 ; Zhao & Liu, 2018 ; Han et al, 2019 ; Yang et al, 2020b , 2021 ).…”

Section: Introductionmentioning

confidence: 99%

A novel network security situation assessment model based on multiple strategies whale optimization algorithm and bidirectional GRU

Zhang,

Fu,

et al. 2023

PeerJ Computer Science

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The rapid development of the internet has brought about a comprehensive transformation in human life. However, the challenges of cybersecurity are becoming increasingly severe, necessitating the implementation of effective security mechanisms. Cybersecurity situational awareness can effectively assess the network status, facilitating the formulation of better cybersecurity defense strategies. However, due to the low accuracy of existing situational assessment methods, situational assessment remains a challenge. In this study, a new situational assessment method, MSWOA-BiGRU, combining optimization algorithms and temporal neural networks, was proposed. Firstly, a scientific indicator system proposed in this research is used to calculate the values of each indicator. Then, the Analytic Hierarchy Process is used to derive the actual situation values, which serve as labels. Taking into account the temporal nature of network traffic, the BiGRU model is utilized for cybersecurity situational assessment. After integrating time-related features and network traffic characteristics, the situational assessment value is obtained. During the evaluation process, a whale optimization algorithm (MSWOA) improved with a mix of strategies proposed in this study was employed to optimize the model. The performance of the proposed MSWOA-BiGRU model was evaluated on publicly available real network security datasets. Experimental results indicate that compared to traditional optimization algorithms, the optimization performance of MSWOA has seen significant enhancement. Furthermore, MSWOA-BiGRU demonstrates superior performance in cybersecurity situational assessment compared to existing evaluation methods.

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A BIPMU-based network security situation assessment method for wireless network

Cited by 10 publications

References 28 publications

Enhanced Internet of Things Security Situation Assessment Model with Feature Optimization and Improved SSA-LightGBM

Enhanced Internet of Things Security Situation Assessment Model with Feature Optimization and Improved SSA-LightGBM

AHP-Based Network Security Situation Assessment for Industrial Internet of Things

A novel network security situation assessment model based on multiple strategies whale optimization algorithm and bidirectional GRU

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