CLC number: TP391.4
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2019-03-14
Cited: 0
Clicked: 6248
Bin Li, Yi-jie Wang, Dong-sheng Yang, Yong-mou Li, Xing-kong Ma. FAAD: an unsupervised fast and accurate anomaly detection method for a multi-dimensional sequence over data stream[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(3): 388-404.
@article{title="FAAD: an unsupervised fast and accurate anomaly detection method for a multi-dimensional sequence over data stream",
author="Bin Li, Yi-jie Wang, Dong-sheng Yang, Yong-mou Li, Xing-kong Ma",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="20",
number="3",
pages="388-404",
year="2019",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1800038"
}
%0 Journal Article
%T FAAD: an unsupervised fast and accurate anomaly detection method for a multi-dimensional sequence over data stream
%A Bin Li
%A Yi-jie Wang
%A Dong-sheng Yang
%A Yong-mou Li
%A Xing-kong Ma
%J Frontiers of Information Technology & Electronic Engineering
%V 20
%N 3
%P 388-404
%@ 2095-9184
%D 2019
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1800038
TY - JOUR
T1 - FAAD: an unsupervised fast and accurate anomaly detection method for a multi-dimensional sequence over data stream
A1 - Bin Li
A1 - Yi-jie Wang
A1 - Dong-sheng Yang
A1 - Yong-mou Li
A1 - Xing-kong Ma
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
IS - 3
SP - 388
EP - 404
%@ 2095-9184
Y1 - 2019
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1800038
Abstract: Recently, sequence anomaly detection has been widely used in many fields. Sequence data in these fields are usually multi-dimensional over the data stream. It is a challenge to design an anomaly detection method for a multi-dimensional sequence over the data stream to satisfy the requirements of accuracy and high speed. It is because: (1) Redundant dimensions in sequence data and large state space lead to a poor ability for sequence modeling; (2) anomaly detection cannot adapt to the high-speed nature of the data stream, especially when concept drift occurs, and it will reduce the detection rate. On one hand, most existing methods of sequence anomaly detection focus on the single-dimension sequence. On the other hand, some studies concerning multi-dimensional sequence concentrate mainly on the static database rather than the data stream. To improve the performance of anomaly detection for a multi-dimensional sequence over the data stream, we propose a novel unsupervised fast and accurate anomaly detection (FAAD) method which includes three algorithms. First, a method called “information calculation and minimum spanning tree cluster” is adopted to reduce redundant dimensions. Second, to speed up model construction and ensure the detection rate for the sequence over the data stream, we propose a method called “random sampling and subsequence partitioning based on the index probabilistic suffix tree.” Last, the method called 𠇊nomaly buffer based on model dynamic adjustment” dramatically reduces the effects of concept drift in the data stream. FAAD is implemented on the streaming platform Storm to detect multi-dimensional log audit data. Compared with the existing anomaly detection methods, FAAD has a good performance in detection rate and speed without being affected by concept drift.
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