scholarly journals Research on Attack Detection Method of Microgrid Central Controller Based on Convolutional Neural Network

2020 ◽  
Vol 1646 ◽  
pp. 012076
Author(s):  
Wei Xi ◽  
Shengguo He ◽  
Rui Chen ◽  
Yongjun Xu ◽  
Weifan Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Detection Method ◽  
Attack Detection ◽  
Central Controller

scholarly journals Convolutional neural network based evil twin attack detection in WiFi networks

2021 ◽  
Vol 336 ◽  
pp. 08006
Author(s):  
Yinghua Tian ◽  
Sheng Wang ◽  
Long Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Detection Method ◽  
Attack Detection ◽  
Classification Model ◽  
Wifi Networks ◽  
Wifi Signal

Evil Twin Attack (ETA) refers to attackers use a device to impersonate a legitimate hotspot. To address the problem of ETAs in the WiFi network, a Convolutional Neural Network (CNN) attack detection method is proposed. The method uses the preamble of the WiFi signal as the feature and uses it to train a CNN based classification model. Next, it uses the trained model to detect the potential ETA device by the inconsistent of the identity it claims and the signal feature. Experiments based on the commercial hardware demonstrate that the proposed method can effectively detect the Evil Twin Attack.

scholarly journals A Real-time Driver Fatigue Detection Method Based on Two-Stage Convolutional Neural Network

2020 ◽  
Vol 53 (2) ◽  
pp. 15374-15379
Author(s):  
Hu He ◽  
Xiaoyong Zhang ◽  
Fu Jiang ◽  
Chenglong Wang ◽  
Yingze Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Detection Method ◽  
Driver Fatigue ◽  
Two Stage ◽  
Fatigue Detection

scholarly journals Spectral Convolution Feature-Based SPD Matrix Representation for Signal Detection Using a Deep Neural Network

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 949
Author(s):  
Jiangyi Wang ◽  
Min Liu ◽  
Xinwu Zeng ◽  
Xiaoqiang Hua
Keyword(s):  
Neural Network ◽  
Signal Detection ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Detection Method ◽  
Learning Algorithm ◽  
Simulated Data ◽  
Data Sets ◽  
Feature Maps ◽  
Simulated Data Sets

Convolutional neural networks have powerful performances in many visual tasks because of their hierarchical structures and powerful feature extraction capabilities. SPD (symmetric positive definition) matrix is paid attention to in visual classification, because it has excellent ability to learn proper statistical representation and distinguish samples with different information. In this paper, a deep neural network signal detection method based on spectral convolution features is proposed. In this method, local features extracted from convolutional neural network are used to construct the SPD matrix, and a deep learning algorithm for the SPD matrix is used to detect target signals. Feature maps extracted by two kinds of convolutional neural network models are applied in this study. Based on this method, signal detection has become a binary classification problem of signals in samples. In order to prove the availability and superiority of this method, simulated and semi-physical simulated data sets are used. The results show that, under low SCR (signal-to-clutter ratio), compared with the spectral signal detection method based on the deep neural network, this method can obtain a gain of 0.5–2 dB on simulated data sets and semi-physical simulated data sets.

scholarly journals Saliency Contour Extraction Based on Multi-level Feature Channel Optimization Coding

2021 ◽  
Vol 7 (4) ◽  
pp. 117
Author(s):  
Linling Fang ◽  
Yingle Fan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Detection Method ◽  
Gabor Filter ◽  
Contour Detection ◽  
Frequency Separation ◽  
Experimental Sample ◽  
Multi Level ◽  
End To End

<p>A biomimetic vision computing model based on multi-level feature channel optimization coding is proposed and applied to image contour detection, combining the end-to-end detection method of full convolutional neural network and the traditional contour detection method based on biological vision mechanism. Considering the effectiveness of the Gabor filter in perceiving the scale and direction of the image target, the Gabor filter is introduced to simulate the multi-level feature response on the visual path. The optimal scale and direction of the Gabor filter are obtained based on the similarity index, and they are used as the frequency separation parameter of the NSCT transform. The contour sub-image obtained by the NSCT transform is combined with the original image for feature enhancement and fusion to realize the primary contour response. The low-dimensional and low-redundancy primary contour response is used as the input sample of the network model to relieve network pressure and reduce computational complexity. A fully improved convolutional neural network model is constructed for multi-scale training, through feature encoder to feature decoder, to achieve end-to-end pixel prediction, and obtain a complete and continuous detection image of the subject contour. Using the BSDS500 atlas as the experimental sample, the average accuracy index is 0.85, which runs on the device CPU at a detection rate of 20+ FPS to achieve a good balance between training efficiency and detection effect.</p>

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