A Critical Study on the Impact of Missing Data Imputation for Classifying Intrusions in Cyber-Physical Water Systems

Document Type

Conference Proceeding

Publication Date

10-13-2021

Publication Title

IECON Proceedings (Industrial Electronics Conference)

Volume

2021-October

Keywords

cyber-physical water system, cyber-security, Intrusion classification, missing data imputation

Abstract

The performance of intrusion classification systems is often hampered by the presence of missing values in data collected from cyber-physical systems. Therefore, it is of paramount importance to robustly handle such missing scores, which in turn enhances the efficiency of intrusion classification task, and, consequently, the cybersecurity of cyber-physical systems. To this aim, this paper studies the efficacy of missing data imputation techniques for safeguarding intrusion classification systems against missing scores. To do this, a hybrid intrusion classification system is designed that comprises several advanced imputation techniques. To evaluate this intrusion classification framework, various incomplete scenarios have been simulated from data collected from a cyber-physical water system. In total, forty-four incomplete scenarios are considered throughout the experiments. The evaluation is conducted based on the classification accuracy and F-measure, as well as the root mean square error of the imputed data. The experimental results indicate the efficiency of the proposed intrusion classification system and find the best match missing data imputation technique for the sake of intrusion classification.

DOI

10.1109/IECON48115.2021.9589513

ISBN

9781665435543

Link to Full Text

Share

COinS