|Table of Contents|

Network traffic detection and analysis based on big data flow(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2017年03期
Page:
294-
Research Field:
Publishing date:

Info

Title:
Network traffic detection and analysis based on big data flow
Author(s):
Cheng Weihua1Zhao Jun2Wu Peng1
1.Jiangsu Electric Power Information Technology Co.,Ltd.,Nanjing 210024,China; 2.State Grid Jiangsu Electric Power Company,Nanjing 210029,China
Keywords:
data packet analysis anomaly detection big data flow network traffic detection distributed stream processing mechanism big data platform distributed storage
PACS:
TP393.08
DOI:
10.14177/j.cnki.32-1397n.2017.41.03.004
Abstract:
A new network traffic detection and analysis system is proposed for network traffic anomaly detection problem.A distributed stream processing mechanism is used to achieve a real-time detection ability.Network data distributed storage is achieved and a network protocol feature library is trained by using the distributed storage and the data computational analysis ability of a big data platform.The network system of detection and analysis gains a good performance in the business of marketing,operation and dispatching in Jiangsu Electric Power Company,and provides a good support for the analysis of various business scenarios.

References:

[1] Ho C Y,Lai Y C,Chen I W,et al.Statistical analysis of false positives and false negatives from real traffic with intrusion detection/prevention systems[J].IEEE Communications Magazine,2012,50(3):146-154.
[2]Chen Xueyun,Xiang Shiming,Liu Chenglin,et al.Vehicle detection in satellite images by hybrid deep convolutional neural networks[J].IEEE Geoscience and Remote Sensing Letters,2014,11(10):1797-1801.
[3]Mukesh K G,Khanna H P,Velvizhi R V.An anamoly based intrusion detection system for mobile ad-hoc networks using genetic algorithm based support vector machine[J].Advances in Natural and Applied Sciences,2015,9(12):40-45.
[4]Khaled O,Marín A,Almenares F,et al.Analysis of secure TCP/IP profile in 61850 based substation automation system for smart grids[J].International Journal of Distributed Sensor Networks,2016,2:1-11.
[5]王元卓,靳小龙,程学旗.网络大数据:现状与展望[J].计算机学报,2013,36(6):1125-1138.
Wang Yuanzhuo,Jin Xiaolong,Cheng Xueqi.Network big data:Present and future[J].Chinese Journal of Computers,2013,36(6):1125-1138.
[6]Thimma M,Liu F,Lin J Q,et al.HyXAC:Hybrid XML access control integrating view-based and query-rewriting approaches[J].IEEE Transactions on Knowledge and Data Engineering,2015,27(8):2190-2202.
[7]臧天宁,云晓春,张永铮.网络设备协同联动模型[J].计算机学报,2011,34(2):216-228.
Zang Tianning,Yun Xiaochun,Zhang Yongzheng.A model of network device coordinative run[J].Chinese Journal of Computers,2011,34(2):216-228.
[8]Li Yuchong,Luo Xingguo,Li Bainan.Detecting network-wide traffic anomalies based on robust multivariate probabilistic calibration model[C]//Military Communications Conference,MILCOM 2015.New York,NY,USA:IEEE,2015:1323-1328.
[9]黄伟,陈昊,郭雅娟.融合领域知识的网络异常检测方法[J].南京理工大学学报,2016,40(2):229-235.
Huang Wei,Chen Hao,Guo Yajuan.Network anomaly detection approach using domain knowledge[J].Journal of Nanjing University of Science and Technology,2016,40(2):229-235.
[10]郏琨琪,何光宇.智能用电网络数据采集与通信机制的研究[J].中国电机工程学报,2016,36(6):1544-1551.
Jia Kunqi,He Guangyu.Research of smart electric appliance network data collection and communication mechanism[J].Proceeding of the Chinese Society of Electrical Engineering,2016,36(6):1544-1551.
[11]Gao Yun,Fu Xiao,Luo Bin,et al.Haddle:A framework for investigating data leakage attacks in hadoop[C]//2015 IEEE Global Communications Conference(GLOBECOM).New York,NY,USA:IEEE,2015:1-6.
[12]Box G E P,Jenkins G M,Reinsel G C,et al.Time series analysis:Forecasting and control[M].5th ed.New Jersey,USA:Wiley,2015.
[13]Zhou J,Kwan C,Ayhan B,et al.A novel cluster kernel RX algorithm for anomaly and change detection using hyperspectral images[J].IEEE Transactions on Geoscience and Remote Sensing,2016,54(11):6497-6504.
[14]Wang Shuihua,Yang Xiaojun,Zhang Yudong,et al.Identification of green,oolong and black teas in China via wavelet packet entropy and fuzzy support vector machine[J].Entropy,2015,17(10):6663-6682.

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Last Update: 2017-06-30