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Optimization of the Novelty Detection Model Based on LSTM Autoencoder for ICS Environment

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Intelligent Systems Applications in Software Engineering (CoMeSySo 2019 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1046))

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The recent evolution in cybersecurity shows how vulnerable our technology is. In addition, contemporary society becoming more reliant on “vulnerable technology”. This is especially relevant in case of critical information infrastructure, which is vital to retain the functionality of modern society. Furthermore, the cyber-physical systems as Industrial control systems are an essential part of critical information infrastructure; and therefore, need to be protected. This article presents a comprehensive optimization methodology in the field of industrial network anomaly detection. We introduce a recurrent neural network preparation for a one-class classification task. In order to optimize the recurrent neural network, we adopted a genetic algorithm. The main goal is to create a robust predictive model in an unsupervised manner. Therefore, we use hyperparameter optimization according to the validation loss function, which defines how well the machine learning algorithm models the given data. To achieve this goal, we adopted multiple techniques as data preprocessing, feature reduction, genetic algorithm, etc.

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This work was funded by the Internal Grant Agency (IGA/FAI/2019/002) and supported by the research project VI20172019054 “An analytical software module for the real-time resilience evaluation from point of the converged security”, supported by the Ministry of the Interior of the Czech Republic in the years 2017–2019. Finally, we thank Lemay and Fernandez [14] who provides ICS datasets.

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Correspondence to Jan Vavra .

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Vavra, J., Hromada, M. (2019). Optimization of the Novelty Detection Model Based on LSTM Autoencoder for ICS Environment. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Intelligent Systems Applications in Software Engineering. CoMeSySo 2019 2019. Advances in Intelligent Systems and Computing, vol 1046. Springer, Cham.

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