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A Review of Meta-level Learning in the Context of Multi-component, Multi-level Evolving Prediction Systems [article]

Abbas Raza Ali, Marcin Budka, Bogdan Gabrys
2020 arXiv   pre-print
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

Haofan Zhang, Ke Nian, Thomas F. Coleman, Yuying Li
2018 International Journal of Data Science and Analytics  
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

Mikolaj Komisarek, Marek Pawlicki, Rafal Kozik, Michal Choras
2021 Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications  
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]

Kyeong-Joong Jeong, Jin-Duk Park, Kyusoon Hwang, Seong-Lyun Kim, Won-Yong Shin
2022 arXiv   pre-print
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]

Hadi Hojjati, Thi Kieu Khanh Ho, Narges Armanfard
2022 arXiv   pre-print
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]

Firuz Kamalov, Ho Hon Leung
2019 arXiv   pre-print
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

Narjes Davari, Bruno Veloso, Gustavo de Assis Costa, Pedro Mota Pereira, Rita P Ribeiro, João Gama
2021 Sensors  
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

Karina Gibert, Miquel Sànchez–Marrè, Joaquín Izquierdo, Karina Gibert
2016 AI Communications  
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

Daniel Ramotsoela, Adnan Abu-Mahfouz, Gerhard Hancke
2018 Sensors  
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]

Nicola Bui, Matteo Cesana, S. Amir Hosseini, Qi Liao, Ilaria Malanchini, Joerg Widmer
2017 arXiv   pre-print
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]

Zhaoyi Xu, Joseph Homer Saleh
2020 arXiv   pre-print
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

Shachar Siboni, Asaf Cohen
2020 Entropy  
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

Muhammad Fahim, Alberto Sillitti
2019 IEEE Access  
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

Xu Zhang, Qingwei Lin, Yong Xu, Si Qin, Hongyu Zhang, Bo Qiao, Yingnong Dang, Xinsheng Yang, Qian Cheng, Murali Chintalapati, Youjiang Wu, Ken Hsieh (+6 others)
2019 USENIX Annual Technical Conference  
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]

Wei Luo, Tongzhi Niu, Lixin Tang, Wenyong Yu, Bin Li
2022 arXiv   pre-print
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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