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A Review of Meta-level Learning in the Context of Multi-component, Multi-level Evolving Prediction Systems
[article]
2020
arXiv
pre-print
Preserved Fulltext
algorithms or their combination, 3) adaptivity mechanisms and their parameters, 4) recurring concept extraction, and 5) concept drift detection. ...
So there is a need for an intelligent recommendation engine that can advise what is the best learning algorithm for a dataset. ...
Active MLL method, in combination with Uncertainty Sampling and outlier detection, had been proposed by [72] to support the selection of informative and anomaly-free Meta-examples for MLL. ...
arXiv:2007.10818v1
fatcat:4jzeippeyjbxfdbza3h5222xey
Spectral ranking and unsupervised feature selection for point, collective, and contextual anomaly detection
2018
International Journal of Data Science and Analytics
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However, in dealing with contextual anomaly problems with different contexts defined by different feature subsets, SRA and other popular methods are still not sufficient on their own. ...
Many algorithms have been devised to address anomaly detection of a specific type from various application domains. ...
Moreover, we notice that most unsupervised anomaly detection algorithms themselves are generally incomplete in dealing with feature-contextual anomalies. ...
doi:10.1007/s41060-018-0161-7
fatcat:xzbxwr3ujfckbnbi2bza2a4mqq
Machine Learning Based Approach to Anomaly and Cyberattack Detection in Streamed Network Traffic Data
2021
Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications
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At the heart of the engine are machine learning algorithms implemented using the TensorFlow library, providing the cutting edge in network intrusion detection. ...
The tool allows easy definition of streams and implementation of any machine learning algorithm. ...
This work has been also supported by the SIMARGL Project -Secure Intelligent Methods for Advanced RecoGnition of malware and stegomalware, with the support of the European Commission and the Horizon 2020 ...
doi:10.22667/jowua.2021.03.31.003
dblp:journals/jowua/KomisarekPKC21
fatcat:u67e3qikzrau3aky6e7tdr5u5y
Two-Stage Deep Anomaly Detection with Heterogeneous Time Series Data
[article]
2022
arXiv
pre-print
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detection (TDAD) framework in which two different unsupervised learning models are adopted depending on types of signals. ...
In Stage I, we select anomaly candidates by using a model trained by operation cycle signals; in Stage II, we finally detect abnormal events out of the candidates by using another model, which is suitable ...
An AE-based algorithm was proposed in [27] for anomaly detection in high-performance computing (HPC) systems. ...
arXiv:2202.05093v1
fatcat:g6exve32wfh3pf4ixls7tnf6w4
Self-Supervised Anomaly Detection: A Survey and Outlook
[article]
2022
arXiv
pre-print
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Over the past few years, anomaly detection, a subfield of machine learning that is mainly concerned with the detection of rare events, witnessed an immense improvement following the unprecedented growth ...
This paper aims to review the current approaches in self-supervised anomaly detection. We present technical details of the common approaches and discuss their strengths and drawbacks. ...
This research was enabled in part by support provided by Calcul Quebec and Compute Canada. ...
arXiv:2205.05173v2
fatcat:es7dkinhvrf7bepowfbbnj4hz4
Outlier Detection in High Dimensional Data
[article]
2019
arXiv
pre-print
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In particular, outlier detection algorithms perform poorly on data set of small size with a large number of features. ...
In this paper, we propose a novel outlier detection algorithm based on principal component analysis and kernel density estimation. ...
As such these are standard outlier detection algorithms used in industry and academia. The ABOD method was initially chosen as a high dimensional benchmark method. ...
arXiv:1909.03681v1
fatcat:wquxibulmjdwdmv37k2uum5tiq
A Survey on Data-Driven Predictive Maintenance for the Railway Industry
2021
Sensors
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The monitoring and logging of industrial equipment events, like temporal behavior and fault events-anomaly detection in time-series-can be obtained from records generated by sensors installed in different ...
parts of an industrial plant. ...
Acknowledgments: This research was carried out in the context of the project FailStopper (DSAIPA / DS /0086/2018). ...
doi:10.3390/s21175739
pmid:34502630
pmcid:PMC8434101
fatcat:i32hsdsqxzeuhbh4jtkkalo7bq
A survey on pre-processing techniques: Relevant issues in the context of environmental data mining
2016
AI Communications
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In this paper a survey on most popular pre-processing steps required in environmental data analysis is presented, together with a proposal to systematize it. ...
One of the important issues related with all types of data analysis, either statistical data analysis, machine learning, data mining, data science or whatever form of data-driven modeling, is data quality ...
Section 6 deals with outliers. Section 7 is on error detection. Section 8 on missing data. Section 9 on relevance and redundancy detection and dimensionality reduction. ...
doi:10.3233/aic-160710
fatcat:nszfc2amj5gy3pntjmquntvlpu
A Survey of Anomaly Detection in Industrial Wireless Sensor Networks with Critical Water System Infrastructure as a Case Study
2018
Sensors
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Anomaly detection is a branch of intrusion detection that is resource friendly and provides broader detection generality making it ideal for IWSN applications. ...
The increased use of Industrial Wireless Sensor Networks (IWSN) in a variety of different applications, including those that involve critical infrastructure, has meant that adequately protecting these ...
Anomaly detection schemes however deal with raw system data at the control level. ...
doi:10.3390/s18082491
pmid:30071595
fatcat:bpperb7vbfb7dhkl2fy2d3xqxe
A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques
[article]
2017
arXiv
pre-print
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In particular, we identify the main prediction and optimization tools adopted in this body of work and link them with objectives and constraints of the typical applications and scenarios. ...
This survey collects and analyzes recent papers leveraging context information to forecast the evolution of network conditions and, in turn, to improve network performance. ...
In the rest of this section we organize the papers dealing with geographic context according to their main focus: the majority of them deals with pure geographical prediction and differs on secondary aspects ...
arXiv:1606.00191v3
fatcat:me4ufu7gsjcmtcrs3m6g4jf2am
Machine Learning for Reliability Engineering and Safety Applications: Review of Current Status and Future Opportunities
[article]
2020
arXiv
pre-print
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Machine learning (ML) pervades an increasing number of academic disciplines and industries. ...
We first provide an overview of the different ML categories and sub-categories or tasks, and we note several of the corresponding models and algorithms. ...
An excellent survey of anomaly detection can be found in Ref. [44] . ...
arXiv:2008.08221v1
fatcat:qhbkiepabfaz7afhctqutncheq
Anomaly Detection for Individual Sequences with Applications in Identifying Malicious Tools
2020
Entropy
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We apply the algorithm to key problems in computer security, as well as a benchmark anomaly detection data set, all using simple, single-feature time-indexed data. ...
In this work, we propose a universal anomaly detection algorithm for one-dimensional time series that is able to learn the normal behaviour of systems and alert for abnormalities, without assuming anything ...
Threshold Analysis In most anomaly detection applications, labeled data are scarce, if any exist; hence, it is not clear how to set the threshold for detection. ...
doi:10.3390/e22060649
pmid:33286421
pmcid:PMC7517183
fatcat:m6c4qqo3bzbbzazvun7gls34li
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. ...
Anomaly detection has attracted considerable attention from the research community in the past few years due to the advancement of sensor monitoring technologies, low-cost solutions, and high impact in ...
, and industrial system in the context of anomalies detection, analysis and prediction. ...
doi:10.1109/access.2019.2921912
fatcat:k7pmdn6ruzevrpyibo7dmqh3ee
Cross-dataset Time Series Anomaly Detection for Cloud Systems
2019
USENIX Annual Technical Conference
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In this paper, we propose cross-dataset anomaly detection: detect anomalies in a new unlabelled dataset (the target) by training an anomaly detection model on existing labelled datasets (the source). ...
However, given the velocity, volume, and diversified nature of cloud monitoring data, it is difficult to obtain sufficient labelled data to build an accurate anomaly detection model. ...
Acknowledgement We thank Professor Mickey Gabel (University of Toronto) for the valuable and constructive suggestions on this paper. ...
dblp:conf/usenix/ZhangLXQ0QDYCCW19
fatcat:yjqa4yvr5vdhldvo2uzrlapgku
Clear Memory-Augmented Auto-Encoder for Surface Defect Detection
[article]
2022
arXiv
pre-print
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In surface defect detection, due to the extreme imbalance in the number of positive and negative samples, positive-samples-based anomaly detection methods have received more and more attention. ...
Secondly, a general artificial anomaly generation algorithm is proposed to simulate anomalies that are as realistic and feature-rich as possible. ...
Therefore, anomaly detection based on positive samples without labels have received more and more attention. ...
arXiv:2208.03879v1
fatcat:dm2mfw7qhveknfbshfc2owstjy
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