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Deep Federated Anomaly Detection for Multivariate Time Series Data
[article]
2022
arXiv
pre-print
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Despite the fact that many anomaly detection approaches have been developed for multivariate time series data, limited effort has been made on federated settings in which multivariate time series data ...
In this paper, we investigate the problem of federated unsupervised anomaly detection and present a Federated Exemplar-based Deep Neural Network (Fed-ExDNN) to conduct anomaly detection for multivariate ...
Introduction Anomaly detection in multivariate time series refers to identifying abnormal status in certain time steps of the time series data [1, 2] . ...
arXiv:2205.04041v1
fatcat:aeuvv3tqk5e6bbh3sddx6ctu5a
Federated Variational Learning for Anomaly Detection in Multivariate Time Series
[article]
2021
arXiv
pre-print
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and temporal dependencies in the multivariate time series data for representation learning and downstream anomaly detection tasks. ...
Anomaly detection has been a challenging task given high-dimensional multivariate time series data generated by networked sensors and actuators in Cyber-Physical Systems (CPS). ...
In the future, we plan to investigate solutions to tackle the data heterogeneity problem in the federated deep generative model for time series anomaly detection. ...
arXiv:2108.08404v2
fatcat:jq2liwv24fhn7hkvgwbv667n5u
A Comprehensive Study of Anomaly Detection Schemes in IoT Networks Using Machine Learning Algorithms
2021
Sensors
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In this paper, we aim to provide an in-depth review of existing works in developing anomaly detection solutions using machine learning for protecting an IoT system. ...
We also indicate that blockchain-based anomaly detection systems can collaboratively learn effective machine learning models to detect anomalies. ...
The IoT anomaly detection using univariate series compares current data against historical time series. ...
doi:10.3390/s21248320
pmid:34960414
pmcid:PMC8708212
fatcat:vogif3xvhbdvvb324iwpyq5vkm
DeepFIB: Self-Imputation for Time Series Anomaly Detection
[article]
2021
arXiv
pre-print
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Time series (TS) anomaly detection (AD) plays an essential role in various applications, e.g., fraud detection in finance and healthcare monitoring. ...
To tackle this problem, we propose a novel self-supervised learning technique for AD in time series, namely DeepFIB. ...
A deep neural network for unsupervised anomaly detection and diagnosis in multivariate
time series data. AAAI, abs/1811.08055, 2019.
R. Zhang, P. Isola, and A.A. Efros. ...
arXiv:2112.06247v1
fatcat:k7gmfcsnajafdp5qgwq4iuq4f4
TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data
[article]
2022
arXiv
pre-print
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Efficient anomaly detection and diagnosis in multivariate time-series data is of great importance for modern industrial applications. ...
Despite the recent developments of deep learning approaches for anomaly detection, only a few of them can address all of these challenges. ...
We thank Kate Highnam for constructive comments on improving the manuscript writing. We thank the providers of all datasets used in this work. ...
arXiv:2201.07284v6
fatcat:eenjcq4u6bd6rk4vwxkts2gokm
Unsupervised Anomaly Detection in Flight Data Using Convolutional Variational Auto-Encoder
2020
Aerospace (Basel)
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To address this challenge, we develop a Convolutional Variational Auto-Encoder (CVAE), an unsupervised deep generative model for anomaly detection in high-dimensional time-series data. ...
precision and recall of detecting anomalies. ...
We would also like to thank Hamed Valizadegan, Thomas Templin, Daniel Weckler, and Marc-Henri Bleu-Laine for their insights and comments in developing and testing the algorithm. ...
doi:10.3390/aerospace7080115
fatcat:4a2pwunjijckzptfzm3bcbnm7m
Data science and AI in FinTech: An overview
[article]
2021
arXiv
pre-print
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blockchain, and the DSAI techniques including complex system methods, quantitative methods, intelligent interactions, recognition and responses, data analytics, deep learning, federated learning, privacy-preserving ...
The research on data science and AI in FinTech involves many latest progress made in smart FinTech for BankingTech, TradeTech, LendTech, InsurTech, WealthTech, PayTech, RiskTech, cryptocurrencies, and ...
frequency and wavelet and Fourier transform for audio and time-series data. ...
arXiv:2007.12681v2
fatcat:jntzuwaktjg2hmmjypi5lvyht4
IoTSDA: IoT-Edge analytics architecture for anomaly detection of univariate health data
2021
Turkish Journal of Electrical Engineering and Computer Sciences
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Presence of abnormal data points can create noise in a time series data set. ...
This article aims at detection of anomalies from a time series 7 health data set collected from a pulse sensor integrated IoT-based device. We use the seasonal-decomposition by Loess 8 i.e. ...
Deep anomaly detection for time-series data in 24 industrial iot: a communication-efficient on-device federated learning approach. ...
doi:10.3906/elk-2106-87
fatcat:lg5ijzqzfza2loyghh4vxd5vxm
Double deep q-learning with prioritized experience replay for anomaly detection in smart environments
2022
IEEE Access
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Our proposed anomaly detector directly learns a decision-making function, which can classify rare events based on multivariate sequential time series data. ...
In this work, we propose the adaption of a deep reinforcement learning algorithm, namely double deep q-learning (DDQN), for anomaly detection in smart environments. ...
support of the National Research Center for Applied Cybersecurity ATHENE. ...
doi:10.1109/access.2022.3179720
fatcat:2qbap4vggbfj7jslhldq6zuiem
Table of Contents
2020
2020 International Conference on Data Mining Workshops (ICDMW)
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Time
Evolving Data (CLEATED 2020)
An Unsupervised Methodology for Online Drift Detection in Multivariate Industrial Datasets 392
Sarah Klein (Data and AI Competence Lab (EluciDATA Lab), Sirris) and ...
Detection of Periodic Multivariate Time Series Under High Acquisition Frequency Scene in IoT 543 Shuo Zhang (Harbin Institute of Technology, China), XiaoFei Chen (Harbin Institute of Technology, China ...
doi:10.1109/icdmw51313.2020.00004
fatcat:ykrkkp5hx5asrpvw6r3oo4rwcq
Unsupervised Anomaly Detection of Industrial Robots using Sliding-Window Convolutional Variational Autoencoder
2020
IEEE Access
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and temporally by coping with multivariate time series data. ...
Besides, the used technique for anomaly detection of robots should be required to not only capture the temporal dependency in collected time series data, but also the inter-correlations between different ...
Since multivariate time series have the same 2-dimensional data structures as image, CNN for analyzing images is suitable for handling multivariate time series as well.
IV. ...
doi:10.1109/access.2020.2977892
fatcat:5a74eirhvfdkdho2mb7uzz7ehy
TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks
[article]
2020
arXiv
pre-print
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TadGAN is trained with cycle consistency loss to allow for effective time-series data reconstruction. ...
However, detecting anomalies in time series data is particularly challenging due to the vague definition of anomalies and said data's frequent lack of labels and highly complex temporal correlations. ...
Cuesta-Infante is funded by the Spanish Government research fundings RTI2018-098743-B-I00 (MICINN/FEDER) and Y2018/EMT-5062 (Comunidad de Madrid). ...
arXiv:2009.07769v3
fatcat:6grbkqexcvehzbpt6vh5bedkpq
A Federated Learning Approach to Anomaly Detection in Smart Buildings
[article]
2021
arXiv
pre-print
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These devices sense the environment and generate multivariate temporal data of paramount importance for detecting anomalies and improving the prediction of energy usage in smart buildings. ...
However, detecting these anomalies in centralized systems is often plagued by a huge delay in response time. ...
Detecting anomalies has become a central research question in IoT applications, particularly from IoT time-series data [10, 20, 34] . ...
arXiv:2010.10293v3
fatcat:zmwqhebvrbgcrgc3pjr6eqchcu
Privacy reinforcement learning for faults detection in the smart grid
2021
Ad hoc networks
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Fault detection in these types of energy systems has recently shown lots of interest in the data science community, where anomalous behavior from energy platforms is identified. ...
A B S T R A C T Recent anticipated advancements in ad hoc Wireless Mesh Networks (WMN) have made them strong natural candidates for Smart Grid's Neighborhood Area Network (NAN) and the ongoing work on ...
[13] developed an anomaly detection approach for mixed-type time series data. ...
doi:10.1016/j.adhoc.2021.102541
fatcat:e3y7ugsccnhhxiewjyu4y5ckby
Smart anomaly detection in sensor systems: A multi-perspective review
2020
Information Fusion
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2020 pre-print arXiv:2010.14946v1 fatcat:6b2mcpbdbbecri4j3bmhejlntaOther Versions
We taxonomize methods ranging from conventional techniques (statistical methods, time-series analysis, signal processing, etc.) to data-driven techniques (supervised learning, reinforcement learning, deep ...
Anomaly detection is concerned with identifying data patterns that deviate remarkably from the expected behaviour. ...
RNN and LSTM have been demonstrated to perform well in detecting anomalies in multivariate time series sensor data [60] . Goh et al. ...
doi:10.1016/j.inffus.2020.10.001
fatcat:r65qp56ipzebnasd33o3wxkfo4
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