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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F19%3A63522682" target="_blank" >RIV/70883521:28140/19:63522682 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-030-30329-7_28" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-30329-7_28</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

The recent evolution in cybersecurity shows how vulnerable our technology is. In addition, contemporary society becoming more reliant on &quot;vulnerable technology&quot;. 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.

Název v anglickém jazyce

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

Popis výsledku anglicky

The recent evolution in cybersecurity shows how vulnerable our technology is. In addition, contemporary society becoming more reliant on &quot;vulnerable technology&quot;. 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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/VI20172019054" target="_blank" >VI20172019054: Analytický programový modul pro hodnocení odolnosti v reálném čase z hlediska konvergované bezpečnosti</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Advances in Intelligent Systems and Computing (Vol. 1)

ISBN

978-3-030-30328-0

ISSN

2194-5357

e-ISSN

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Počet stran výsledku

14

Strana od-do

306-319

Název nakladatele

Springer Verlag

Místo vydání

Berlín

Místo konání akce

Zlín

Datum konání akce

3. 10. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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