MSJAD: Multi-Source Joint Anomaly Detection of Web Application Access

被引:0
作者
Chen, Xinxin [1 ]
Wang, Jing [1 ]
Wang, Xingyu [2 ]
Wang, Chengsen [2 ]
Lv, Guosong [1 ]
Li, Jiankun [1 ]
Chen, Dewei [1 ]
Wu, Bo [1 ]
Li, LianYuan [1 ]
Yu, Wei [1 ]
机构
[1] China Mobile Res Inst, Beijing, Peoples R China
[2] Beijing Univ Posts & Telecommun, Beijing, Peoples R China
来源
2022 18TH INTERNATIONAL CONFERENCE ON MOBILITY, SENSING AND NETWORKING, MSN | 2022年
关键词
time series; anomaly detection; slow access of web applications; anomaly samples; feature processing;
D O I
10.1109/MSN57253.2022.00080
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Fixed broadband internet service can provide a stable broadband network of up to 100 megabits or even gigabits and users at home can use fixed broadband service for all kinds of internet surfing, including website and application access, watching videos, playing games, etc. Traditional maintenance for fixed broadband networks primarily uses human manual methods, supplemented by some low-level semi-automation operations. Since the long processes with numerous network elements in the fixed broadband network, it is difficult for traditional operation and maintenance to support effectively with high quality. When abnormalities occur, it is quite manpower cost and time cost to monitor and locate faults. Therefore, to improve the autonomous capability of the fixed broadband network, intelligent operation and maintenance methods are necessary. First of all, a brand-new data pre-process method is proposed to detect anomalies and problems of slow access by selecting web services access commonly visited by users. Secondly, as the fixed broadband network is a multi-level and complex structure with only a small amount of anomaly sample data, we propose a multi-source joint anomaly detection model called MSJAD model on multi-dimensional features data. The model validation results on real datasets from the real fixed broadband network are state-of-the-art. The accuracy rate reaches 98% and the recall is over 99%. We have already begun to deploy the model on the real fixed broadband network and have achieved good feedback.
引用
收藏
页码:461 / 468
页数:8
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