A Critical Study on the Impact of Missing Data Imputation for Classifying Intrusions in Cyber-Physical Water Systems
| Autor: | Ehsan Hallaji, Roozbeh Razavi-Far, Maryam Farajzadeh-Zanjani, Ranim Aljoudi, Mehrdad Saif |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | IECON Electrical and Computer Engineering Publications |
| DOI: | 10.1109/iecon48115.2021.9589513 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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