Detection of IoT Cyberattacks in Smart Cities: A Comparative Analysis of Deep Learning and Ensemble Learning Methods

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Detection of IoT Cyberattacks in Smart Cities: A Comparative Analysis of Deep Learning and Ensemble Learning Methods

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Detection of IoT Cyberattacks in Smart Cities: A Comparative Analysis of Deep Learning and Ensemble Learning Methods

Popis výsledku anglicky

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.

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

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

Novel and Intelligent Digital Systems (NiDS 2024)

ISBN

978-3-031-73343-7

ISSN

2367-3370

e-ISSN

2367-3389

Počet stran výsledku

12

Strana od-do

549-560

Název nakladatele

Springer Nature Switzerland AG

Místo vydání

Cham

Místo konání akce

Athény

Datum konání akce

25. 9. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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