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Learning to Detect Anomalous Wireless Links in IoT Networks
[article]
2020
arXiv
pre-print
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We study the performance of threshold- and machine learning (ML)-based classifiers to automatically detect these anomalies. ...
performing models based on F1 score, supervised ML models outperform the unsupervised counterpart models with about 18% on average for anomaly types SuddenD and SuddenR, and this trend also applies to ...
autoencoder which conforms with the findings of anomaly detection on time-series data [19] , [51] . ...
arXiv:2008.05232v1
fatcat:dbkefl5qm5b7hlgxksk5w5voqy
Unsupervised Prediction of Negative Health Events Ahead of Time
[article]
2019
arXiv
pre-print
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In recent years, unsupervised learning methods have drawn special attention of researchers to tackle the sparse annotation of health data and real-time detection of anomalies has been a central problem ...
The emergence of continuous health monitoring and the availability of an enormous amount of time series data has provided a great opportunity for the advancement of personal health tracking. ...
Sequential Representation Learning In time series data, temporal features carry important information. ...
arXiv:1901.11168v1
fatcat:djan77dwmnfoxlj42aiey7aqfy
Deep Learning for Anomaly Detection: A Survey
[article]
2019
arXiv
pre-print
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The aim of this survey is two-fold, firstly we present a structured and comprehensive overview of research methods in deep learning-based anomaly detection. ...
Furthermore, we review the adoption of these methods for anomaly across various application domains and assess their effectiveness. ...
Multi-variate time series deep anomaly detection Anomaly detection in multivariate time series data is a challenging task. ...
arXiv:1901.03407v2
fatcat:x3tb4ccxfvdkfo7k2y2oxhr7ly
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 ...
The local model is trained based on the time series data collected on the l-th local device for unsupervised anomaly detection. ...
arXiv:2205.04041v1
fatcat:aeuvv3tqk5e6bbh3sddx6ctu5a
Learning to Detect Anomalous Wireless Links in IoT Networks
2020
IEEE Access
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ACKNOWLEDGMENT The authors would like to recognize Tomaž Šolc, one of the core developers of the LOG-a-TEC testbed for his contribution to the motivation of this work. ...
For this anomaly type, time-series and FFT domain may not be the optimal data representations for the sake of developing a reliable and non-overfitting model. ...
[26] proposed recurrent autoencoders for time series anomaly detection for IoT networks. However, they used a synthetic dataset with metrics derived from several Yahoo services. ...
doi:10.1109/access.2020.3039333
fatcat:ndbcalivavhipn66eklglziyua
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
Warping Resilient Scalable Anomaly Detection in Time Series
[article]
2021
arXiv
pre-print
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for time series data. ...
Within the key stage of representation learning, we employ data augmentation through bespoke time series operators which are passed through a twin autoencoder architecture to learn warping-robust representations ...
The work is supported by project titled "Robust Multiview Learning for Extreme Events Detection and Prediction in Time Series Data", IITPKD/2021/013/CSE/SAB funded by ICSR ...
arXiv:1906.05205v2
fatcat:yse2nf5d45akzdytutkd63lf5i
Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature Review
2019
IEEE Access
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anomaly detection, prediction, and analysis. ...
In this paper, we present the results of a systematic literature review about anomaly detection techniques except for these dominant research areas. ...
time-series data is generated from a number of components and attached sensors. ...
doi:10.1109/access.2019.2921912
fatcat:k7pmdn6ruzevrpyibo7dmqh3ee
A Review of Machine Learning and Deep Learning Techniques for Anomaly Detection in IoT Data
2021
Applied Sciences
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The nature of data, anomaly types, learning mode, window model, datasets, and evaluation criteria are also presented. ...
Anomaly detection has gained considerable attention in the past couple of years. ...
Techniques
Nature of
the Data
Types of
Anomaly
Anomaly
Detection Types
Windowing
Dataset
Evaluation
Criteria
LSTMs [40]
Time-Series
Point
anomaly
Supervised
learning using
deep learning ...
doi:10.3390/app11125320
fatcat:cjbzetn3xbb3tlm7lebaglujei
SVD-GAN for Real-Time Unsupervised Video Anomaly Detection
2021
Zenodo
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Real-time unsupervised anomaly detection from videos is challenging due to the uncertainty in occurrence and definition of abnormal events. ...
To overcome this ambiguity, an unsupervised adversarial learning model is proposed to detect such unusual events. ...
Acknowledgement This project has received funding from the European Union's Horizon 2020 research and innovation programme, under grant agreement No. 779813 (SARAS). ...
doi:10.5281/zenodo.6299656
fatcat:pzmblyrrgvb4jevguuiouk32ty
Anomalous Example Detection in Deep Learning: A Survey
[article]
2021
arXiv
pre-print
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This survey tries to provide a structured and comprehensive overview of the research on anomaly detection for DL based applications. ...
To make DL more robust, several posthoc (or runtime) anomaly detection techniques to detect (and discard) these anomalous samples have been proposed in the recent past. ...
Time Series and Video Surveillance Anomaly Detection -The task of detecting anomalies in multivariate time series data is quite challenging. ...
arXiv:2003.06979v2
fatcat:4mogo75b4rbxrc6vph2xmllkue
Time series anomaly detection based on shapelet learning
2018
Computational statistics (Zeitschrift)
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This article presents a novel method for unsupervised anomaly detection based on the shapelet transformation for time series. ...
We consider the problem of learning to detect anomalous time series from an unlabeled data set, possibly contaminated with anomalies in the training data. ...
This is the first approach that combines shapelet features for time series, shapelet learning, and unsupervised anomaly detection. ...
doi:10.1007/s00180-018-0824-9
fatcat:x6tfzrqvtfblpnuf7thpzms3by
Enhancing Unsupervised Anomaly Detection with Score-Guided Network
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2109.04684v1 fatcat:4zopkef2bfgbhdafclfsnwddce
Anomaly detection plays a crucial role in various real-world applications, including healthcare and finance systems. ...
Owing to the limited number of anomaly labels in these complex systems, unsupervised anomaly detection methods have attracted great attention in recent years. ...
detection tasks with image, time-series, or graph datasets. ...
arXiv:2109.04684v2
fatcat:ktdv33ntaffndjmkppn2iiqccm
Anomalous Example Detection in Deep Learning: A Survey
2020
IEEE Access
Preserved Fulltext
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The file type is application/pdf.
This survey tries to provide a structured and comprehensive overview of the research on anomaly detection for DL based applications. ...
To make DL more robust, several posthoc (or runtime) anomaly detection techniques to detect (and discard) these anomalous samples have been proposed in the recent past. ...
Time Series and Video Surveillance Anomaly Detection -The task of detecting anomalies in multivariate time series data is quite challenging. ...
doi:10.1109/access.2020.3010274
fatcat:3xjpfc64nvcbtfpwtwwbitjuvm
Tensor-Based ECG Anomaly Detection toward Cardiac Monitoring in the Internet of Health Things
2021
Sensors
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Obtained principal components are used as features for anomaly detection using machine learning models (e.g., deep support vector data description (deep SVDD)) as well as statistical control charts (e.g ...
Realizing the full potential of IoHT-enabled cardiac monitoring hinges, to a great extent, on the detection of disease-induced anomalies from collected ECGs. ...
Data Availability Statement: Publicly available datasets were analyzed in this study. ...
doi:10.3390/s21124173
fatcat:dlxfoq33mzeyrl2g7in5ent7yu
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