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

Result code in 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>
Result on the web
<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
—

Result language
angličtina
Original language name
Optimization of the Novelty Detection Model Based on LSTM Autoencoder for ICS Environment
Original language description
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.
Czech name
—
Czech description
—

Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/VI20172019054" target="_blank" >VI20172019054: An analitical software module for the real-time resilience evaluation from point of the converged security</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Advances in Intelligent Systems and Computing (Vol. 1)
ISBN
978-3-030-30328-0
ISSN
2194-5357
e-ISSN
—
Number of pages
14
Pages from-to
306-319
Publisher name
Springer Verlag
Place of publication
Berlín
Event location
Zlín
Event date
Oct 3, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—

Similar results(10)