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Neural Processing Letters
Published in: Neural Processing Letters 3/2021

17-03-2021

Multi-object Spatial–Temporal Anomaly Detection Using an LSTM-Based Framework

Authors: Jin Ning, Leiting Chen, Chuan Zhou, Defu Liu

Published in: Neural Processing Letters | Issue 3/2021

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Abstract

Spatial–temporal anomaly detection methods are mostly used for single object, but rarely for multiple objects with changing positions. This problem is often encountered in multi-player online battle arena (MOBA) games, train control systems and modern battlefield command systems, and so on. However, due to the time dependence, object correlation and Display Constraint, there are few methods for solving such problem properly. In this paper, we defined the problem of multi-object spatial–temporal anomaly detection with Display Constraint in detail. To address this problem, we proposed a long short-term memory (LSTM)-based framework. First, we proposed a Display Constraint Graph to represent location relationship and designed an LSTM framework to calculate the reconstruction error. Then we used the DCG based anomaly score to discriminate abnormal subsequences and objects. We applied this method to 18 MOBA game data streams, and achieved better results than traditional methods.
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Literature
1.
Breunig MM, Kriegel HP, Ng RT, Sander J (2000) LOF: identifying density-based local outliers. ACM Sigmod Record 29(2):93–104CrossRef
2.
de Cheveigné A, Arzounian D (2018) Robust detrending, rereferencing, outlier detection, and inpainting for multichannel data. NeuroImage 172:903–912CrossRef
3.
Duggimpudi MB, Abbady S, Chen J, Raghavan VV (2017) Spatio-temporal outlier detection algorithms based on computing behavioral outlierness factor. Data Knowl Eng 122:1–24CrossRef
4.
Guerbai Y, Chibani Y, Hadjadji B (2015) The effective use of the one-class SVM classifier for handwritten signature verification based on writer-independent parameters. Pattern Recognit 48(1):103–113CrossRef
5.
Guo J, Zhu W (2018) Partial multi-view outlier detection based on collective learning. In: Thirty-second AAAI conference on artificial intelligence
6.
Han M, Ding J, Li J (2017) TDMCS: an efficient method for mining closed frequent patterns over data streams based on time decay model. Int Arab J Inf Technol 14(6):851–860
7.
Hong C, Yu J, Wan J, Tao D, Wang M (2015) Multimodal deep autoencoder for human pose recovery. IEEE Trans Image Process 24(12):5659–5670MathSciNetCrossRef
8.
Hong C, Yu J, Zhang J, Jin X, Lee K (2019) Multimodal face-pose estimation with multitask manifold deep learning. IEEE Trans Ind Inf 15(7):3952–3961CrossRef
9.
Huang J, Zhu Q, Yang L, Feng J (2016) A non-parameter outlier detection algorithm based on natural neighbor. Knowl-Based Syst 92:71–77CrossRef
10.
Kathareios G, Anghel A, Mate A, Clauberg R, Gusat M (2017) Catch it if you can: real-time network anomaly detection with low false alarm rates. In: 2017 16th IEEE international conference on machine learning and applications (ICMLA). IEEE, pp 924–929
11.
Kieu T, Yang B, Guo C, Jensen CS (2019) Outlier detection for time series with recurrent autoencoder ensembles. In: 28th international joint conference on artificial intelligence
12.
Kieu T, Yang B, Jensen CS (2018) Outlier detection for multidimensional time series using deep neural networks. In: 2018 19th IEEE international conference on mobile data management (MDM). IEEE, pp 25–134
13.
Laina I, Rupprecht C, Belagiannis V, Tombari F, Navab N (2016) Deeper depth prediction with fully convolutional residual networks. In: Computer vision and pattern recognition, pp 239–248
14.
Li K, Li S, Ding Z, Zhang W, Fu Y (2018) Latent discriminant subspace representations for multi-view outlier detection. In: Thirty-second AAAI conference on artificial intelligence
15.
Li S, Shao M, Fu Y (2018) Multi-view low-rank analysis with applications to outlier detection. ACM Trans Knowl Discov Data (TKDD) 12(3):32
16.
Liu FT, Ting KM, Zhou ZH (2008) Isolation forest. In: 2008 eighth IEEE international conference on data mining. IEEE, pp 413–422
17.
Lu H, Liu Y, Fei Z, Guan C (2018) An outlier detection algorithm based on cross-correlation analysis for time series dataset. IEEE Access 6:53593–53610CrossRef
18.
Malhotra P, Ramakrishnan A, Anand G, Vig L, Agarwal P, Shroff G (2016) LSTM-based encoder-decoder for multi-sensor anomaly detection. In: 33rd International Conference on Machine Learning. New York, pp 1–5
19.
Na GS, Kim D, Yu H (2018) Dilof: Effective and memory efficient local outlier detection in data streams. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. ACM, pp 1993–2002
20.
Pokrajac D, Lazarevic A, Latecki LJ (2007) Incremental local outlier detection for data streams. In: IEEE symposium on computational intelligence and data mining
21.
Qin X, Cao L, Rundensteiner EA, Madden S (2019) Scalable kernel density estimation-based local outlier detection over large data streams. In: EDBT, pp 421–432
22.
Ren H, Liu M, Li Z, Pedrycz W (2017) A piecewise aggregate pattern representation approach for anomaly detection in time series. Knowl-Based Syst 135:29–39CrossRef
23.
24.
Yu J, Tan M, Zhang H, Tao D, Rui Y (2019) Hierarchical deep click feature prediction for fine-grained image recognition. IEEE Trans Pattern Anal Mach Intell 41:1–14CrossRef
25.
Yu J, Tao D, Wang M, Rui Y (2015) Learning to rank using user clicks and visual features for image retrieval. IEEE Trans Syst Man Cybernet 45(4):767–779
26.
Yu J, Yao J, Zhang J, Yu Z, Tao D (2020) Sprnet: Single-pixel reconstruction for one-stage instance segmentation. IEEE Trans Syst Man Cybernet 1:1–12
27.
Yun U, Kim D, Yoon E, Fujita H (2018) Damped window based high average utility pattern mining over data streams. Knowl-Based Syst 144:188–205CrossRef
28.
Zhao G, Yu Y, Song P, Zhao G, Ji Z (2018) A parameter space framework for online outlier detection over high-volume data streams. IEEE Access 6:38124–38136CrossRef
29.
Zhou Y, Zou H, Arghandeh R, Gu W, Spanos CJ (2018) Non-parametric outliers detection in multiple time series a case study: Power grid data analysis. In: Thirty-second AAAI conference on artificial intelligence
30.
Zhu Z, Yao D, Huang J, Li H, Bi J (2018) Sub-trajectory-and trajectory-neighbor-based outlier detection over trajectory streams. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, Berlin, pp 551–563
Metadata
Title
Multi-object Spatial–Temporal Anomaly Detection Using an LSTM-Based Framework
Authors
Jin Ning
Leiting Chen
Chuan Zhou
Defu Liu
Publication date
17-03-2021
Publisher
Springer US
Published in
Neural Processing Letters / Issue 3/2021
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-021-10456-3

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