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AD(2)S: Adaptive anomaly detection on sporadic data streams

文献类型：期刊论文

作者Liu, Fengrui1,3; Wang, Yang1,3; Li, Zhenyu1,3; Guan, Hongtao1,3; Xie, Gaogang1,2
刊名COMPUTER COMMUNICATIONS
出版日期2023-09-01
卷号209页码:151-162
ISSN号0140-3664
关键词Anomaly detection Sporadic data Data streams Quality of service
DOI10.1016/j.comcom.2023.06.027
英文摘要With the widespread use of Internet applications, ensuring the quality and reliability of online services has become increasingly important. Therefore, anomaly detection methods play a critical role in identifying potential anomalies in the data streams of infrastructure systems and service applications. However, most of known detection methods have an underlying assumption that the data streams are continuous. In practice, we learn that many real-world data streams can be sporadic. It incurs particular challenges for the task of anomaly detection, for which the common preprocessing of downsampling on sporadic data can omit potential anomalies and delay alarms. In this paper, we propose an ensemble learning-based anomaly detection method on sporadic data streams named AD2S. It consists of two modules: a monitor module to continuously and adaptively determine the measure windows for observations, and a detection module that utilizes an isolation partition strategy to estimate the anomaly degree of each incoming observation. Based on experimental results on eight synthetic and public real-world datasets, our method outperforms other state-of-the-art methods with an average AUC score of 0.923. Additionally, our analysis demonstrates that the proposed method has constant amortized time and space complexity, enabling once detection within an average of 9.9 ms and maximum memory usage of 26.14 KB. The code of AD2S is open-sourced for further research.
资助项目National Key Ramp;D Program of China[2020YFB1805603] ; CAS-Austria Joint Project[171111KYSB20200001]
WOS研究方向Computer Science ; Engineering ; Telecommunications
语种英语
出版者ELSEVIER
WOS记录号WOS:001040198100001
源URL[http://119.78.100.204/handle/2XEOYT63/21313]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Xie, Gaogang
作者单位1.Univ Chinese Acad Sci, Beijing, Peoples R China
2.Chinese Acad Sci, Comp Network Informat Ctr, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Fengrui,Wang, Yang,Li, Zhenyu,et al. AD(2)S: Adaptive anomaly detection on sporadic data streams[J]. COMPUTER COMMUNICATIONS,2023,209:151-162.
APA Liu, Fengrui,Wang, Yang,Li, Zhenyu,Guan, Hongtao,&Xie, Gaogang.(2023).AD(2)S: Adaptive anomaly detection on sporadic data streams.COMPUTER COMMUNICATIONS,209,151-162.
MLA Liu, Fengrui,et al."AD(2)S: Adaptive anomaly detection on sporadic data streams".COMPUTER COMMUNICATIONS 209(2023):151-162.

入库方式： OAI收割

来源：计算技术研究所

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