scholarly journals Large-Scale and Robust Intrusion Detection Model Combining Improved Deep Belief Network With Feature-Weighted SVM

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 98600-98611 ◽  
Author(s):  
Yukun Wu ◽  
Wei William Lee ◽  
Zhicheng Xu ◽  
Minya Ni
Keyword(s):  
Intrusion Detection ◽  
Large Scale ◽  
Deep Belief Network ◽  
Belief Network ◽  
Detection Model ◽  
Model Combining

scholarly journals Fast outlier detection for high-dimensional data of wireless sensor networks

2020 ◽  
Vol 16 (10) ◽  
pp. 155014772096383
Author(s):  
Yan Qiao ◽  
Xinhong Cui ◽  
Peng Jin ◽  
Wu Zhang
Keyword(s):  
Wireless Sensor Networks ◽  
Support Vector Machine ◽  
Sensor Networks ◽  
Outlier Detection ◽  
Large Scale ◽  
Deep Belief Network ◽  
Wireless Sensor ◽  
High Dimensional ◽  
Support Vector ◽  
Belief Network

This article addresses the problem of outlier detection for wireless sensor networks. As increasing amounts of observational data are tending to be high-dimensional and large scale, it is becoming increasingly difficult for existing techniques to perform outlier detection accurately and efficiently. Although dimensionality reduction tools (such as deep belief network) have been utilized to compress the high-dimensional data to support outlier detection, these methods may not achieve the desired performance due to the special distribution of the compressed data. Furthermore, because most existed classification methods must solve a quadratic optimization problem in their training stage, they cannot perform well in large-scale datasets. In this article, we developed a new form of classification model called “deep belief network online quarter-sphere support vector machine,” which combines deep belief network with online quarter-sphere one-class support vector machine. Based on this model, we first propose a model training method that learns the radius of the quarter sphere by a sorting method. Then, an online testing method is proposed to perform online outlier detection without supervision. Finally, we compare the proposed method with the state of the arts using extensive experiments. The experimental results show that our method not only reduces the computational cost by three orders of magnitude but also improves the detection accuracy by 3%–5%.

scholarly journals Online Recognition Method for Voltage Sags Based on a Deep Belief Network

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 43 ◽  
Author(s):  
Fei Mei ◽  
Yong Ren ◽  
Qingliang Wu ◽  
Chenyu Zhang ◽  
Yi Pan ◽  
...  
Keyword(s):  
Large Scale ◽  
Deep Belief Network ◽  
Support Vector ◽  
Voltage Sag ◽  
Voltage Sags ◽  
Data Sampling ◽  
Recognition Method ◽  
Belief Network ◽  
Online Recognition ◽  
Residential Electricity

Voltage sag is a serious power quality phenomenon that threatens industrial manufacturing and residential electricity. A large-scale monitoring system has been established and continually improved to detect and record voltage sag events. However, the inefficient process of data sampling cannot provide valuable information early enough for governance of the system. Therefore, a novel online recognition method for voltage sags is proposed. The main contributions of this paper include: 1) The causes and waveform characters of voltage sags were analyzed; 2) according to the characters of different sag waveforms, 10 voltage sag characteristic parameters were proposed and proven to be effective; 3) a deep belief network (DBN) model was built using these parameters to complete automatic recognition of the sag event types. Experiments were conducted using voltage sag data from one month recorded by the 10 kV monitoring points in Suqian, Jiangsu Province, China. The results showed good performance of the proposed method: Recognition accuracy was 96.92%. The test results from the proposed method were compared to the results from support vector machine (SVM) recognition methods. The proposed method was shown to outperform SVM.

scholarly journals An Optimization Method for Intrusion Detection Classification Model Based on Deep Belief Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 87593-87605 ◽  
Author(s):  
Peng Wei ◽  
Yufeng Li ◽  
Zhen Zhang ◽  
Tao Hu ◽  
Ziyong Li ◽  
...  
Keyword(s):  
Intrusion Detection ◽  
Optimization Method ◽  
Deep Belief Network ◽  
Classification Model ◽  
Belief Network ◽  
Model Based

A Method of Intrusion Detection in Large-Scale Digital Media Communication Network

2014 ◽  
Vol 602-605 ◽  
pp. 2019-2022
Author(s):  
Yan Zhen Cao
Keyword(s):  
Intrusion Detection ◽  
Digital Media ◽  
Large Scale ◽  
Support Vector ◽  
Media Communication ◽  
Broadcast Television ◽  
Detection Model ◽  
Classification Technique ◽  
High Classification Accuracy ◽  
The Media

With the development of network, an increasing amount of broadcast television information transforms from simulation into digit, which therefor make the security of media information an imminent issue to be concerned. In this paper, a new kind of intrusion detection model was designed for the media information security system. In the system, both the false alarm rate and missing report rate decreased by using support vector machine classification technique in this new model. As a result of the experimental results, our algorithm processed a high classification accuracy and efficiency.

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