A Novel Approach to Network Security Situation Assessment Based on Attack Confidence

Liu, Donghang; Liang, Dong; Lv, Shaoqing; Dong, Ying; He, Fannv; Wu, Chensi; Zhang, Yuqing; Ma, Hua

doi:10.1007/978-3-319-64701-2_33

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…The perception of this method was more comprehensive, and the ability was more powerful than the traditional network security situational awareness method. Liu et al [Liu, Dong and Lv (2017)] proposed the confidence-based situation awareness method, which uses integrated learning algorithm and D-S evidence theory to calculate confidence of attack and combines various situational elements to obtain situation information. Lounis [Lounis (2018)] proposed a stochastic security evaluation method which used a tree model to establish related security scenarios, then used a stochastic model to evaluate security situation.…”

Section: Related Workmentioning

confidence: 99%

A DDoS Attack Situation Assessment Method via Optimized Cloud Model Based on Influence Function

Tang¹,

Zheng²,

Cheng³

et al. 2019

Computers, Materials &Amp; Continua

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The existing network security situation assessment methods cannot effectively assess the Distributed denial-of-service (DDoS) attack situation. In order to solve these problems, we propose a DDoS attack situation assessment method via optimized cloud model based on influence function. Firstly, according to the state change characteristics of the IP addresses which are accessed by new and old user respectively, this paper defines a fusion feature value. Then, based on this value, we establish a V-Support Vector Machines (V-SVM) classification model to analyze network flow for identifying DDoS attacks. Secondly, according to the change of new and old IP addresses, we propose three evaluation indexes. Furthermore, we propose index weight calculation algorithm to measure the importance of different indexes. According to the fusion index, which is optimized by the weighted algorithm, we define the Risk Degree (RD) and calculate the RD value of each network node. Then we obtain the situation information of the whole network according to the RD values, which are from each network nodes with different weights. Finally, the whole situation information is classified via cloud model to quantitatively assess the DDoS attack situation. The experimental results show that our method can not only improve the detection rate and reduce the missing rate of DDoS attacks, but also access the DDoS attack situation effectively. This method is more accurate and flexible than the existing methods.

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Section: Related Workmentioning

confidence: 99%

A DDoS Attack Situation Assessment Method via Optimized Cloud Model Based on Influence Function

Tang¹,

Zheng²,

Cheng³

et al. 2019

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…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 ). Although the characteristics of current network traffic are undeniably important, in the real world, network traffic doesn’t appear as independent entities but rather as a large volume of network traffic occurring in a time-series manner.…”

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 Novel Approach to Network Security Situation Assessment Based on Attack Confidence

Cited by 2 publications

References 9 publications

A DDoS Attack Situation Assessment Method via Optimized Cloud Model Based on Influence Function

A DDoS Attack Situation Assessment Method via Optimized Cloud Model Based on Influence Function

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

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