Detection of IoT Cyberattacks in Smart Cities: A Comparative Analysis of Deep Learning and Ensemble Learning Methods
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F24%3A39922251" target="_blank" >RIV/00216275:25410/24:39922251 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-73344-4_47" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-73344-4_47</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-73344-4_47" target="_blank" >10.1007/978-3-031-73344-4_47</a>
Alternative languages
Result language
angličtina
Original language name
Detection of IoT Cyberattacks in Smart Cities: A Comparative Analysis of Deep Learning and Ensemble Learning Methods
Original language description
In this study, we embarked on a comparative investigation of Deep Learning (DL) techniques and ensemble learning approaches for enhancing IoT security. Specifically, we scrutinized the performance of Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Random Forest (RF), and AdaBoost models evaluated to both binary and specific attack vector classifications. The imbalanced and voluminous datasets of UNSW-NB15 and CICIDS2017 were employed for evaluation. The empirical evidence gleaned from our experiments suggests that RF exhibits superior efficacy over its counterparts, with accuracy and F1-score in the range of 99.68% to 99.90%. Within the DL paradigm, the MLP model achieved the highest F1-score (99.17%) and the lowest False Positive Rate (FPR) of 0.0037 using UNSW-NB15, among DL models. Overall, the proposed models exhibit commendable performance in binary classification tasks. However, this does not indicate their suitability for the detection of all types of attacks, as the individual attack detection result shows. Furthermore, models employed in our work demonstrated superior results as compared to existing models that used smaller sample sizes of these datasets.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Novel and Intelligent Digital Systems (NiDS 2024)
ISBN
978-3-031-73343-7
ISSN
2367-3370
e-ISSN
2367-3389
Number of pages
12
Pages from-to
549-560
Publisher name
Springer Nature Switzerland AG
Place of publication
Cham
Event location
Athény
Event date
Sep 25, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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