Intelligent solutions for real-life data-driven applications
The subject of this thesis belongs to the topic of machine learning or, specifically,
to the development of advanced methods for regression analysis, clustering, and
anomaly detection. Industry is constantly seeking improved production practices and minimized production time and costs. In connection to this, several
industrial case studies are presented in which mathematical models for predicting paper quality were proposed. The most important variables for the prediction
models are selected based on information-theoretic measures and regression trees
approach.
The rest of the original papers are devoted to unsupervised machine learning. The main focus is developing advanced spectral clustering techniques for
community detection and anomaly detection. As part of these efforts, a number of enhancements for the dependence clustering algorithm have been proposed. These enhancements include adding regularization for controlling the
size of clusters, extension to the ensemble version for improving model stability, handling overlapping clusters, and adaptation to solving anomaly detection
problems and handling big datasets.
Another focus of the thesis is on developing anomaly detection algorithms
for network security data. In connection to this, a probabilistic transition-based
approach is proposed for detecting application-layer distributed denial-of-service
attacks.
The developed approaches are tested on real datasets and are capable of efficiently solving the given tasks with high accuracy and good performance. They
are shown to be applicable to solving variable selection, graph segmentation, and
anomaly detection tasks in different applications.
...
Julkaisija
University of JyväskyläISBN
978-951-39-7279-0ISSN Hae Julkaisufoorumista
1456-5390Asiasanat
clustering community detection anomaly detection paper machine regression analysis regression trees mutual information graph segmentation spectral clustering variable selection network security big data koneoppiminen regressioanalyysi klusterianalyysi paperikoneet laadunvalvonta tiedonlouhinta tietoturva
Metadata
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Lisenssi
Samankaltainen aineisto
Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.
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On data mining applications in mobile networking and network security
Zolotukhin, Mikhail (University of Jyväskylä, 2014) -
Effect of variable selection strategy on the predictive models for adverse pregnancy outcomes of pre-eclampsia : A retrospective study
Zheng, Dongying; Hao, Xinyu; Khan, Muhanmmad; Kang, Fuli; Li, Fan; Hämäläinen, Timo; Wang, Lixia (Scholar Media Publishing Company, 2024)Objectives: The improvement of prediction for adverse pregnancy outcomes is quite essential to the women suffering from pre-eclampsia, while the collection of predictive indicators is the prerequisite. The traditional ... -
Unsupervised network intrusion detection systems for zero-day fast-spreading network attacks and botnets
Vahdani Amoli, Payam (University of Jyväskylä, 2015)Today, the occurrence of zero-day and complex attacks in high-speed networks is increasingly common due to the high number vulnerabilities in the cyber world. As a result, intrusions become more sophisticated and fast ... -
Improvements and applications of the elements of prototype-based clustering
Hämäläinen, Joonas (Jyväskylän yliopisto, 2018) -
Intrusion detection applications using knowledge discovery and data mining
Juvonen, Antti (University of Jyväskylä, 2014)
Ellei toisin mainittu, julkisesti saatavilla olevia JYX-metatietoja (poislukien tiivistelmät) saa vapaasti uudelleenkäyttää CC0-lisenssillä.