scholarly journals Neural Network Security: Hiding CNN Parameters with Guided Grad-CAM

2020 ◽  
Author(s):  
Linda Guiga ◽  
A. Roscoe
Keyword(s):  
Neural Network ◽  
Network Security

scholarly journals Analysis and Application of intelligent Power Control System Cyber Security Situation Awareness Based on Wavelet Neural Network

2021 ◽  
Vol 2078 (1) ◽  
pp. 012067
Author(s):  
Jingcheng Zhao ◽  
Xiaomeng Li ◽  
Yaofu Cao ◽  
Junwen Liu ◽  
Junlu Yan ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Network Security ◽  
Power Control ◽  
Situation Awareness ◽  
Wavelet Neural Network ◽  
Control Network ◽  
Industrial Control ◽  
True Value ◽  
Power Control System

Abstract In recent years, international industrial control network security incidents have occurred frequently. As a core component of the industrial control field, intelligent power control systems are increasingly threatened by external network attacks. Based on the current research status of power industrial control network security, closely combining the development of active monitoring and defense technology in the public network field and the problems encountered by network security operators in actual work, this paper uses data mining methods to study the power control system network security situation awareness technology. Combing operational data collection and integrated processing, situation index screening and extraction, we use wavelet neural network analysis method to train the sampled data set, and finally calculate the true value of the network security status through deep intelligent learning. Finally, we conclude that the artificial intelligence algorithm based on wavelet neural network can be used for power control system network security situation awareness. In actual work, it can predict the situation value for a period of time in the future and assist network security personnel in judgment and decision-making.

Deep Neural Network Security From a Hardware Perspective

2021 ◽  
Author(s):  
Tong Zhou ◽  
Yuheng Zhang ◽  
Shijin Duan ◽  
Yukui Luo ◽  
Xiaolin Xu
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Deep Neural Network

scholarly journals Study on Network Security Based on PCA and BP Neural Network Under Green Communication

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 53733-53749 ◽  
Author(s):  
Fengchun Liu ◽  
Wenjie Huo ◽  
Yang Han ◽  
Shichao Yang ◽  
Xiaoyu Li
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Bp Neural Network ◽  
Green Communication

scholarly journals Recurrent Neural Network Model Based on a New Regularization Technique for Real-Time Intrusion Detection in SDN Environments

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Marwan Ali Albahar
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Intrusion Detection ◽  
Recurrent Neural Network ◽  
Network Performance ◽  
Integrated Approach ◽  
Regularization Technique ◽  
Model Based ◽  
Minimum Number ◽  
Sdr Model

Software-defined networking (SDN) is a promising approach to networking that provides an abstraction layer for the physical network. This technology has the potential to decrease the networking costs and complexity within huge data centers. Although SDN offers flexibility, it has design flaws with regard to network security. To support the ongoing use of SDN, these flaws must be fixed using an integrated approach to improve overall network security. Therefore, in this paper, we propose a recurrent neural network (RNN) model based on a new regularization technique (RNN-SDR). This technique supports intrusion detection within SDNs. The purpose of regularization is to generalize the machine learning model enough for it to be performed optimally. Experiments on the KDD Cup 1999, NSL-KDD, and UNSW-NB15 datasets achieved accuracies of 99.5%, 97.39%, and 99.9%, respectively. The proposed RNN-SDR employs a minimum number of features when compared with other models. In addition, the experiments also validated that the RNN-SDR model does not significantly affect network performance in comparison with other options. Based on the analysis of the results of our experiments, we conclude that the RNN-SDR model is a promising approach for intrusion detection in SDN environments.

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