基于机器学习的虚拟网络感知数据缺陷自动检测

doi:10.13229/j.cnki.jdxbgxb20190721

Abstract

Abstract:

To solve the problem that the intersection and parallelism ratio (IOU) of two defect-aware data detection algorithms based on data mining and pattern matching is smaller, which leads to poor detection effect, a new automatic defect detection algorithm for virtual network sensing data is proposed on the basis of machine learning. First, the perceptual data is acquired and processed as the learning samples. Then, a BP neural network model is constructed and trained. Finally, the learning samples are used as the input of the trained BP neural network model to distinguish the defect data from normal data to realize automatic defect detection of perceptual data. The results show that the IOU of machine learning-based automatic defect detection algorithm for virtual network sensing data is 0.9588, which is closer to 1 than that of data mining-based and pattern matching-based defect sensing data detection algorithms, which shows that the detection effect of this algorithm is better.

Key words: machine learning, virtual network, perceived data, automatic defect detection algorithm

CLC Number:

TP311.26

Wei FANG,Yi HUANG,Xin-qiang MA. Automatic defect detection for virtual network perceptual data based on machine learning[J].Journal of Jilin University(Engineering and Technology Edition), 2020, 50(5): 1844-1849.

Figures/Tables 7

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References 21

1	费欢, 肖甫, 李光辉, 等. 基于多模态数据流的无线传感器网络异常检测方法[J]. 计算机学报, 2017, 40(8): 1829-1842.
	Fei Huan, Xiao Fu, Li Guang-hui, et al. An anomaly detection method of wireless sensor network based on multi-modals data stream[J]. Chinese Journal of Computers, 2017, 40(8): 1829-1842.
2	周霆, 虞保忠. 基于感知数据分析的传感器网络覆盖控制[J]. 电子测试, 2017(11): 49-50, 54.
	Zhou Ting, Yu Bao-zhong. A data analysis based coverage control for wireless sensor networks[J]. Electronic Test, 2017(11): 49-50, 54.
3	王玮, 苏琦, 周伟, 等. 不同类别非完整大数据中缺失数据填充算法[J]. 科学技术与工程, 2018, 18(8): 91-96.
	Wang Wei, Su Qi, Zhou Wei, et al. Missing data filling algorithm in different types of incomplete large data[J]. Science Technology and Engineering, 2018, 18(8): 91-96.
4	刘荣海, 耿磊昭, 杨迎春, 等. 基于机器学习的GIS典型缺陷的智能识别研究[J]. 软件, 2017, 38(8): 184-189.
	Liu Rong-hai, Geng Lei-zhao, Yang Ying-chun, et al. Intelligent identification research on the GIS typical defects based on SVM[J]. Computer Engineering & Software, 2017, 38(8): 184-189.
5	龙婧, 刘伟, 殷胜. 基于机器学习的电网设备档案数据异常诊断研究[J]. 电力信息与通信技术, 2018, 16(7): 21-27.
	Long Jing, Liu Wei, Yin Sheng. Research on abnormal diagnosis for power grid equipment archival data based on machine learning[J]. Electric Power Information and Communication Technology, 2018, 16(7): 21-27.
6	杨青. 基于大数据分析的网络异常流量检测[J]. 机械设计与制造工程, 2018, 47(11): 79-82.
	Yang Qing. The network anomaly traffic detection based on large data analysis[J]. Machine Design and Manufacturing Engineering, 2018, 47(11): 79-82.
7	陈胜, 朱国胜, 祁小云, 等. 基于机器学习的网络异常流量检测研究[J]. 信息通信, 2017(12): 39-42.
	Chen Sheng, Zhu Guo-sheng, Qi Xiao-yun, et al. Research on abnormal network traffic detection based on machine learning[J]. Information & Communications, 2017(12): 39-42.
8	Liu W, Li M, Yi L. Identifying children with autism spectrum disorder based on their face processing abnormality:a machine learning framework[J]. Autism Research, 2016, 9(8): 888-898.
9	Boashash B, Ouelha S. Automatic signal abnormality detection using time-frequency features and machine learning: a newborn EEG seizure case study[J]. Knowledge-Based Systems, 2016, 106: 38-50.
10	Lee S Y, Wi S R, Seo E, et al. ProFiOt: Abnormal Behavior Profiling(ABP) of IoT devices based on a machine learning approach[C]∥International Telecommunication Networks & Applications Conference, Melbourne, Australia, 2017: 1-6.
11	Xie S, Zhang X, Jing C. Video crowd detection and abnormal behavior model detection based on machine learning method[J]. Neural Computing and Applications, 2019, 31(12): 175-184.
12	Lee S, Im J, Kim J, et al. Arctic sea ice thickness estimation from CryoSat-2 satellite data using machine learning-based lead detection[J]. Remote Sensing, 2016, 8(9): 698-718.
13	Al-Yaseen W L, Othman Z A, Nazri M Z A. Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system[J]. Expert Systems with Applications, 2017, 67: 296-303.
14	Chang X, Kalet A, Liu S, et al. WE-H-BRC-06: a unified machine-learning based probabilistic model for automated anomaly detection in the treatment plan data[J]. Medical Physics, 2016, 43(6): 3841.
15	金顺福, 郄修尘, 武海星, 等. 基于新型休眠模式的云虚拟机分簇调度策略及性能优化[J]. 吉林大学学报: 工学版, 2020, 50(1): 237-246.
	Jin Shun-fu, Xiu-chen Qie, Wu Hai-xing, et al. Clustered virtual machine allocation strategy in cloud computing based on new type of sleep-mode and performance optimization[J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(1): 237-246.
16	李勇, 黄志球, 王勇, 等. 基于多源数据的跨项目软件缺陷预测[J]. 吉林大学学报: 工学版, 2016, 46(6): 2034-2041.
	Li Yong, Huang Zhi-qiu, Wang Yong, et al. New approach of cross-project defect prediction based on multi-source data[J]. Journal of Jilin University(Engineering and Technology Edition), 2016, 46(6): 2034-2041.
17	林封笑,陈华杰,姚勤炜,等. 基于混合结构卷积神经网络的目标快速检测算法[J]. 计算机工程, 2018, 44(12): 222-227.
	Lin Feng-xiao,Chen Hua-jie,Yao Qin-wei,et al. Target fast detection algorithm based on hybrid structure convolutional neural network[J]. Computer Engineering, 2018, 44(12): 222-227.
18	高东东,张新生. 基于空间卷积神经网络模型的图像显著性检测 [J]. 计算机工程, 2018, 44(5): 240-245.
	Gao Dong-dong,Zhang Xin-sheng. Image saliency detection based on spatial convolutional neural network model [J]. Computer Engineering, 2018, 44(5): 240-245.
19	Al-Jarrah Omar Y,Al-Hammdi Yousof,Yoo Paul D,Muhaidat Sami,et al.Semi-supervised multi-layered clustering model for intrusion detection[J].Digital Communications and Networks,2018(4):277-286
20	Damrath Martin,Peter Adam Hoeher,Gilbert J.M. Forkel.[J].Digital Communications and Networks,2018(2):98-105
21	Lu Cheng-ye,Wu Sheng,Jiang Chun-xiao Jiang,Jinfeng Hu.Weak harmonic signal detection method in chaotic interference based onextended Kalman filter[J].Digital Communications and Networks,,2019,(1):51-55.

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名称		说明
硬件环境	处理器	2 GHz Intel Core i5
硬件环境	内存	8 GB
软件环境	操作系统	macOS Sierra 10.12.6
实验工具		Python2.7
		Numpy1.11.2
		Scikit-learn 0.019.0
		Matplotlib 2.0.2

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Automatic defect detection for virtual network perceptual data based on machine learning

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算法	IOU
基于机器学习的虚拟网络感知数据缺陷自动检测	0.9588
基于数据挖掘的缺陷感知数据检测	0.7456
基于模式匹配的缺陷感知数据检测	0.8145