A network security situation assessment method based on attack intention perception

2016 ◽  
Author(s):  
Kou Guang ◽  
Tang Guangming ◽  
Ding Xia ◽  
Wang Shuo ◽  
Wang Kun
Keyword(s):  
Network Security ◽  
Assessment Method ◽  
Situation Assessment

scholarly journals An efficient method for network security situation assessment

2020 ◽  
Vol 16 (11) ◽  
pp. 155014772097151
Author(s):  
Xiaoling Tao ◽  
Kaichuan Kong ◽  
Feng Zhao ◽  
Siyan Cheng ◽  
Sufang Wang
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Data Storage ◽  
Input Data ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Assessment Method ◽  
Trend Prediction ◽  
Situation Assessment ◽  
Situational Assessment

Network security situational assessment, the core task of network security situational awareness, can obtain security situation by comprehensively analyzing various factors that affect network status. Thus, network security situational assessment can provide accurate security state evaluation and security trend prediction for users. Although plenty of network security situational assessment methods have been proposed, there are still many problems to solve. First, because of high dimensionality of input data, computational complexity in model construction could be very high. Moreover, most of the existing schemes trade computational overhead for accuracy. Second, due to the lack of centralized standard, the weights of indicators are usually determined empirically or by subjective opinions of domain expert. To solve the above problems, we propose a novel network security situation assessment method based on stack autoencoding network and back propagation neural network. In stack autoencoding network and back propagation neural network, to reduce the data storage overhead and improve computational efficiency, we use stack autoencoding network to reduce the dimensions of the indicator data. And the low-dimensional data output by hidden layer of stack autoencoding network will be the input data of the error back propagation neural network. Then, the back propagation neural network algorithm is adopted to perform network security situation assessment. Finally, extensive experiments are conducted to verify the effectiveness of the proposed method.

A Quantitative Network Situation Assessment Method Based on Stochastic Model

2014 ◽  
Vol 513-517 ◽  
pp. 768-771
Author(s):  
Bo Yun Zhang
Keyword(s):  
Network Security ◽  
Stochastic Model ◽  
Evaluation System ◽  
Evaluation Method ◽  
Assessment Method ◽  
Security Evaluation ◽  
Network Systems ◽  
Situation Assessment ◽  
Network Defense ◽  
State Duration

This paper describes the basic models of network security state evaluation system and concentrates on researching the situation assessment method with stochastic model. In the paper, which makes use of the Hidden Semi-Markov Model (HsMM), tries to simulate the operation of network system. The alert statistics, deriving from network defense system, is used as data sources to realize the evaluation of network security situation. HsMM modifies the HMM model concerning the hypothesis of some state-duration time in relation to exponential distribution, which coincides the description of the network systems operation in the real world. The experimental results imply that HsMM is an ideal security evaluation method.

scholarly journals An Efficient Network Security Situation Assessment Method Based on AE and PMU

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiao-ling Tao ◽  
Zi-yi Liu ◽  
Chang-song Yang
Keyword(s):  
Network Security ◽  
Situation Awareness ◽  
Large Scale ◽  
Rapid Development ◽  
Assessment Method ◽  
Security Threat ◽  
Situation Assessment ◽  
Network Attack ◽  
Novel Method ◽  
Indicator Data

Network security situation assessment (NSSA) is an important and effective active defense technology in the field of network security situation awareness. By analyzing the historical network security situation awareness data, NSSA can evaluate the network security threat and analyze the network attack stage, thus fully grasping the overall network security situation. With the rapid development of 5G, cloud computing, and Internet of things, the network environment is increasingly complex, resulting in diversity and randomness of network threats, which directly determine the accuracy and the universality of NSSA methods. Meanwhile, the indicator data is characterized by large scale and heterogeneity, which seriously affect the efficiency of the NSSA methods. In this paper, we design a new NSSA method based on the autoencoder (AE) and parsimonious memory unit (PMU). In our novel method, we first utilize an AE-based data dimensionality reduction method to process the original indicator data, thus effectively removing the redundant part of the indicator data. Subsequently, we adopt a PMU deep neural network to achieve accurate and efficient NSSA. The experimental results demonstrate that the accuracy and efficiency of our novel method are both greatly improved.

A network security situation assessment method based on adversarial deep learning

2021 ◽  
Vol 102 ◽  
pp. 107096
Author(s):  
Hongyu Yang ◽  
Renyun Zeng ◽  
Guangquan Xu ◽  
Liang Zhang
Keyword(s):  
Deep Learning ◽  
Network Security ◽  
Assessment Method ◽  
Situation Assessment
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