Unsupervised Feature Selection for Outlier Detection on Streaming Data to Enhance Network Security

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F21%3A43963616" target="_blank" >RIV/49777513:23520/21:43963616 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.3390/app112412073" target="_blank" >https://doi.org/10.3390/app112412073</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/app112412073" target="_blank" >10.3390/app112412073</a>

Result language

angličtina

Original language name

Unsupervised Feature Selection for Outlier Detection on Streaming Data to Enhance Network Security

Original language description

Over the past couple of years, machine learning methods—especially the outlier detection ones—have anchored in the cybersecurity field to detect network-based anomalies rooted in novel attack patterns. However, the ubiquity of massive continuously generated data streams poses an enormous challenge to efficient detection schemes and demands fast, memory-constrained online algorithms that are capable to deal with concept drifts. Feature selection plays an important role when it comes to improve outlier detection in terms of identifying noisy data that contain irrelevant or redundant features. State-of-the-art work either focuses on unsupervised feature selection for data streams or (offline) outlier detection. Substantial requirements to combine both fields are derived and compared with existing approaches. The comprehensive review reveals a research gap in unsupervised feature selection for the improvement of outlier detection methods in data streams. Thus, a novel algorithm for Unsupervised Feature Selection for Streaming Outlier Detection, denoted as UFSSOD, will be proposed, which is able to perform unsupervised feature selection for the purpose of outlier detection on streaming data. Furthermore, it is able to determine the amount of top-performing features by clustering their score values. A generic concept that shows two application scenarios of UFSSOD in conjunction with off-the-shell online outlier detection algorithms has been derived. Extensive experiments have shown that a promising feature selection mechanism for streaming data is not applicable in the field of outlier detection. Moreover, UFSSOD, as an online capable algorithm, yields comparable results to a state-of-the-art offline method trimmed for outlier detection.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Applied Sciences

ISSN

2076-3417

e-ISSN

—

Volume of the periodical

11

Issue of the periodical within the volume

24

Country of publishing house

CH - SWITZERLAND

Number of pages

30

Pages from-to

1-30

UT code for WoS article

000735828400001

EID of the result in the Scopus database

2-s2.0-85121349927