Hierarchical network threat situation assessment method for DDoS based on D-S evidence theory

2017 ◽  
Author(s):  
Liu Zihao ◽  
Zhang Bin ◽  
Zhu Ning ◽  
Li Lixun
Keyword(s):  
Evidence Theory ◽  
Assessment Method ◽  
Hierarchical Network ◽  
Situation Assessment ◽  
Threat Situation

scholarly journals An Unsupervised Learning-Based Network Threat Situation Assessment Model for Internet of Things

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Hongyu Yang ◽  
Renyun Zeng ◽  
Fengyan Wang ◽  
Guangquan Xu ◽  
Jiyong Zhang
Keyword(s):  
Internet Of Things ◽  
Unsupervised Learning ◽  
Network Traffic ◽  
Test Group ◽  
Reconstruction Error ◽  
Assessment Model ◽  
Generative Adversarial Networks ◽  
Situation Assessment ◽  
Variational Autoencoder ◽  
Threat Situation

With the wide application of network technology, the Internet of Things (IoT) systems are facing the increasingly serious situation of network threats; the network threat situation assessment becomes an important approach to solve these problems. Aiming at the traditional methods based on data category tag that has high modeling cost and low efficiency in the network threat situation assessment, this paper proposes a network threat situation assessment model based on unsupervised learning for IoT. Firstly, we combine the encoder of variational autoencoder (VAE) and the discriminator of generative adversarial networks (GAN) to form the V-G network. Then, we obtain the reconstruction error of each layer network by training the network collection layer of the V-G network with normal network traffic. Besides, we conduct the reconstruction error learning by the 3-layer variational autoencoder of the output layer and calculate the abnormal threshold of the training. Moreover, we carry out the group threat testing with the test dataset containing abnormal network traffic and calculate the threat probability of each test group. Finally, we obtain the threat situation value (TSV) according to the threat probability and the threat impact. The simulation results show that, compared with the other methods, this proposed method can evaluate the overall situation of network security threat more intuitively and has a stronger characterization ability for network threats.

scholarly journals Research on Network Threat and Situation Assessment Method of Electric Power Information System

2021 ◽  
Vol 1883 (1) ◽  
pp. 012105
Author(s):  
Yunhui Liang ◽  
Linghao Zhang ◽  
Sheng Wang ◽  
Jie Zhang ◽  
Juling Zhang ◽  
...  
Keyword(s):  
Information System ◽  
Electric Power ◽  
Assessment Method ◽  
Situation Assessment

scholarly journals A Novel Uncertainty Management Approach for Air Combat Situation Assessment Based on Improved Belief Entropy

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 495 ◽  
Author(s):  
Ying Zhou ◽  
Yongchuan Tang ◽  
Xiaozhe Zhao
Keyword(s):  
Information Fusion ◽  
Evidence Theory ◽  
Uncertainty Management ◽  
Management Approach ◽  
Uncertain Information ◽  
Improved Method ◽  
Situation Assessment ◽  
Air Combat ◽  
Belief Entropy ◽  
Dempster’S Rule Of Combination

Uncertain information exists in each procedure of an air combat situation assessment. To address this issue, this paper proposes an improved method to address the uncertain information fusion of air combat situation assessment in the Dempster–Shafer evidence theory (DST) framework. A better fusion result regarding the prediction of military intention can be helpful for decision-making in an air combat situation. To obtain a more accurate fusion result of situation assessment, an improved belief entropy (IBE) is applied to preprocess the uncertainty of situation assessment information. Data fusion of assessment information after preprocessing will be based on the classical Dempster’s rule of combination. The illustrative example result validates the rationality and the effectiveness of the proposed method.

A Variational Generative Network Based Network Threat Situation Assessment

2020 ◽  
pp. 479-491
Author(s):  
Hongyu Yang ◽  
Renyun Zeng ◽  
Fengyan Wang ◽  
Guangquan Xu ◽  
Jiyong Zhang
Keyword(s):  
Situation Assessment ◽  
Threat Situation

scholarly journals A Situation Assessment Method with an Improved Fuzzy Deep Neural Network for Multiple UAVs

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 194
Author(s):  
Lin Zhang ◽  
Yian Zhu ◽  
Xianchen Shi ◽  
Xuesi Li
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Assessment Method ◽  
Learning Performance ◽  
Stochastic Gradient Descent ◽  
Situation Assessment ◽  
Neural Network Approach ◽  
Assessment Task ◽  
Multiple Uavs ◽  
The Neural Network

To improve the intelligence and accuracy of the Situation Assessment (SA) in complex scenes, this work develops an improved fuzzy deep neural network approach to the situation assessment for multiple Unmanned Aerial Vehicle(UAV)s. Firstly, this work normalizes the scene data based on time series and use the normalized data as the input for an improved fuzzy deep neural network. Secondly, adaptive momentum and Elastic SGD (Elastic Stochastic Gradient Descent) are introduced into the training process of the neural network, to improve the learning performance. Lastly, in the real-time situation assessment task for multiple UAVs, conventional methods often bring inaccurate results for the situation assessment because these methods don’t consider the fuzziness of task situations. This work uses an improved fuzzy deep neural network to calculate the results of situation assessment and normalizes these results. Then, the degree of trust of the current result, relative to each situation label, is calculated with the normalized results using fuzzy logic. Simulation results show that the proposed method outperforms competitors.

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