Subtle Aberration Monitoring of Link Traffic Based on Outlier Detection

doi:10.11871/jfdc.10-1649.2021.06.011

Frontiers of Data and Computing ›› 2021, Vol. 3 ›› Issue (6): 142-150.

doi: 10.11871/jfdc.10-1649.2021.06.011

Previous Articles Next Articles

Subtle Aberration Monitoring of Link Traffic Based on Outlier Detection

LI Jingjing^1,^2,^*(),YANG Xiaolin^1,²(),LI Jun^1,²(),HE Qunhui¹()

1. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100083, China
2. University of Chinese Academy of Sciences, Beijing 100049, China

Received:2021-05-11 Online:2021-12-20 Published:2022-01-26
Contact: LI Jingjing E-mail:jjli@cnic.cn;xlyang@cnic.cn;jlee@cstnet.cn;hqh@cstnet.cn

Abstract

Abstract:

[Objective] The subtle aberration of network traffic has a harmful influence on scientific research precision joint observation. The fixed threshold network traffic monitoring currently used can only effectively warn the link resource consumption, but cannot monitor the subtle aberration of branch line traffic.[Methods] Therefore, we propose a new link traffic warning model which can monitor subtle aberration and trigger alarm based on the improved outlier detection method in this study to support the network management of CSTNet. The model can implement fast monitoring and early warning of subtle aberration of the link traffic by fast calculation of deviation between the observed traffic value and dynamic baseline in sliding time window. [Conclusions] Experiments in the real operation of CSTNet demonstrate that the warning trigger points are all consistent with the abnormal record points in the network operation and diagnostic record, which has the feasibility of engineering application.

Key words: outlier detection, link traffic, monitoring model, dynamic baseline

LI Jingjing,YANG Xiaolin,LI Jun,HE Qunhui. Subtle Aberration Monitoring of Link Traffic Based on Outlier Detection[J]. Frontiers of Data and Computing, 2021, 3(6): 142-150.

Figures/Tables 10

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Table 1

Fig.8

Fig.9

References 11

[1]	陈中, 郑为民, 陈肖. 中国e-VLBI网的建立及应用[J]. 中国科学院上海天文台年刊, 2015,36:136-147.
[2]	Hawkins D. Identification of Outliers[M]. London: Chapman and Hall, 1980: 188.
[3]	王宏鼎, 童云海, 谭少华, 等. 异常点挖掘研究进展[J]. 智能系统学报, 2006,1(1):67-73.
[4]	孙哲南, 张兆翔, 王威, 刘菲, 谭铁牛. 2019年人工智能新态势与新进展[J]. 数据与计算发展前沿, 2019,1(06):1-16.
[5]	Hung W L, Yang M S. An omission approach for detec-ting outliers in fuzzy regression models[J]. Fuzzy sets and systems, 2006,157(23):3109-3122.
[6]	Liu X, Cheng G, Wu J X. Analyzing outliers cautiously[J]. IEEE Transactions on Knowledge and Data Enginee-ring, 2002,14(2):432-437.
[7]	Breunig M M, Kriegel H P, Ng R T, et al. LOF: identi-fying density-based local outliers[C]. Proceedings of the 2000 ACM SIGMOD international conference on Mana-gement of data. 2000: 93-104.
[8]	Jin W Tung AK H, Han J, et al. Ranking outliers using symmetric neighborhood relationship[C]. Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Berlin, Heidelberg, 2006: 577-593.
[9]	杨校林, 李菁菁, 李易. FINFLO:快速局部异常点检测算法. 数据与计算发展前沿, 2020,2(6):82-89.
[10]	苏冰, 王超, 刘岩. 基于固定阈值法的声纳信号检测性能研究[J]. 数字技术与应用, 2012,000(009):85-86.
[11]	郭炜. 基于动态基线的业务运营支撑网异常流量检测研究[C]// 2011年通信与信息技术新进展—第八届中国通信学会学术年会论文集. 2011.

		Actual class
		Positive Class	Negative Class
Fixed class	Positive Class	TP=4	FP=2
Fixed class	Negative Class	FN=1	TN=1433

Subtle Aberration Monitoring of Link Traffic Based on Outlier Detection

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 10

References 11

Related Articles 0

Recommended Articles

Metrics

Comments