In recent years, Internet of Things (IoT) traffic has increased dramatically and is expected to grow further in the next future. Because of their vulnerabilities, IoT devices are often the target of cyber-attacks with dramatic consequences. For this reason, there is a strong need for powerful tools to guarantee a good level of security in IoT networks. Machine and deep learning approaches promise good performance for such a complex task. In this work, we employ state-of-art traffic classifiers based on deep learning and assess their effectiveness in accomplishing IoT attack classification. We aim to recognize different attack classes and distinguish them from benign network traffic. In more detail, we utilize effective and unbiased input data that allow fast (i.e. 'early') detection of anomalies and we compare performance with that of traditional (i.e. 'postmortem') machine learning classifiers. The experimental results highlight the need for advanced deep learning architectures fed with input data specifically tailored and designed for IoT attack classification. Furthermore, we perform an occlusion analysis to assess the influence on the performance of some network layer fields and the possible bias they may introduce.

Machine and Deep Learning Approaches for IoT Attack Classification / Nascita, A.; Cerasuolo, F.; Monda, D. D.; Garcia, J. T. A.; Montieri, A.; Pescape', A.. - (2022), pp. 1-6. (Intervento presentato al convegno 2022 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2022 tenutosi a Virtual nel 2022) [10.1109/INFOCOMWKSHPS54753.2022.9797971].

Machine and Deep Learning Approaches for IoT Attack Classification

Nascita A.;Montieri A.
;
Pescape' A.
2022

Abstract

In recent years, Internet of Things (IoT) traffic has increased dramatically and is expected to grow further in the next future. Because of their vulnerabilities, IoT devices are often the target of cyber-attacks with dramatic consequences. For this reason, there is a strong need for powerful tools to guarantee a good level of security in IoT networks. Machine and deep learning approaches promise good performance for such a complex task. In this work, we employ state-of-art traffic classifiers based on deep learning and assess their effectiveness in accomplishing IoT attack classification. We aim to recognize different attack classes and distinguish them from benign network traffic. In more detail, we utilize effective and unbiased input data that allow fast (i.e. 'early') detection of anomalies and we compare performance with that of traditional (i.e. 'postmortem') machine learning classifiers. The experimental results highlight the need for advanced deep learning architectures fed with input data specifically tailored and designed for IoT attack classification. Furthermore, we perform an occlusion analysis to assess the influence on the performance of some network layer fields and the possible bias they may introduce.
2022
978-1-6654-0926-1
Machine and Deep Learning Approaches for IoT Attack Classification / Nascita, A.; Cerasuolo, F.; Monda, D. D.; Garcia, J. T. A.; Montieri, A.; Pescape', A.. - (2022), pp. 1-6. (Intervento presentato al convegno 2022 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2022 tenutosi a Virtual nel 2022) [10.1109/INFOCOMWKSHPS54753.2022.9797971].
File in questo prodotto:
File Dimensione Formato  
nascita2022machine.pdf

solo utenti autorizzati

Tipologia: Versione Editoriale (PDF)
Licenza: Copyright dell'editore
Dimensione 251.4 kB
Formato Adobe PDF
251.4 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/904866
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 21
  • ???jsp.display-item.citation.isi??? 6
social impact