Extending Machine Learning-Based Intrusion Detection with the Imputation Method
Autor: | Michał Choraś, Rafał Kozik, Mikołaj Komisarek, Piotr Soboński, Aleksandra Pawlicka, Marek Pawlicki |
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Rok vydání: | 2021 |
Předmět: |
Computer science
business.industry 02 engineering and technology Intrusion detection system Machine learning computer.software_genre Missing data Critical infrastructure Stream processing Work (electrical) 020204 information systems Component (UML) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Imputation (statistics) Artificial intelligence Architecture business computer |
Zdroj: | Progress in Image Processing, Pattern Recognition and Communication Systems ISBN: 9783030815226 CORES/IP&C/ACS Lecture Notes in Networks and Systems Lecture Notes in Networks and Systems-Progress in Image Processing, Pattern Recognition and Communication Systems Progress in Image Processing, Pattern Recognition and Communication Systems-Proceedings of the Conference (CORES, IP&C, ACS)-June 28-30 2021 |
ISSN: | 2367-3370 2367-3389 |
DOI: | 10.1007/978-3-030-81523-3_28 |
Popis: | Cybersecurity is relevant to everyone, as cyberthreat concerns individuals and whole societies, and a precise cyberattack targeted at critical infrastructure may pose danger to millions of citizens. At a European level, several initiatives have aimed at protecting CI, one of them being InfraStress. This paper presents a part of the InfraStress architecture, the ST.CD.2 component, which is dedicated to cyberthreat detection. The question of handling missing data, the used dataset and experimental setup have been discussed. The ST.CD.2 component has been demonstrated in industrially relevant environment; the results of this experiment have been provided. When compared with the results achieved by means of the traditional ways of handling missing data, the proposed method proves superior. Then, the final conclusions are given and future work is briefly described. |
Databáze: | OpenAIRE |
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