Extending Machine Learning-Based Intrusion Detection with the Imputation Method

Autor: Michał Choraś, Rafał Kozik, Mikołaj Komisarek, Piotr Soboński, Aleksandra Pawlicka, Marek Pawlicki
Rok vydání: 2021
Předmět:
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