The widespread adoption of the Internet of Things (IoT) in various industries highlights the urgent need for strong security measures to protect data transmission integrity. This article stresses the importance of continuous physical layer authentication (PLA) in wireless networks with mobile nodes, using machine learning (ML) to enhance security. By analyzing unique characteristics in wireless communication channels, recipient nodes can accurately verify sender node authenticity, bolstering overall communication system security. The proposed method addresses challenges in adapting PLA to dynamic mobile scenarios, particularly dealing with channel variations caused by transmitter mobility. The article evaluates the authentication accuracy a ML-based technique. Overall, this research contributes to advancing wireless network security by integrating ML-based continuous PLA to ensure IoT communication reliability and integrity in dynamic mobile environments.

Adaptive Security in Mobile Wireless Networks: Machine Learning-Enhanced Continuous Physical Layer Authentication for Dynamic Environments / Marabissi, Dania; Stomaci, Andrea; Mucchi, Lorenzo. - ELETTRONICO. - (2024), pp. 310-315. (Intervento presentato al convegno 2024 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0 & IoT)) [10.1109/metroind4.0iot61288.2024.10584234].

Adaptive Security in Mobile Wireless Networks: Machine Learning-Enhanced Continuous Physical Layer Authentication for Dynamic Environments

Marabissi, Dania;Stomaci, Andrea;Mucchi, Lorenzo
2024

Abstract

The widespread adoption of the Internet of Things (IoT) in various industries highlights the urgent need for strong security measures to protect data transmission integrity. This article stresses the importance of continuous physical layer authentication (PLA) in wireless networks with mobile nodes, using machine learning (ML) to enhance security. By analyzing unique characteristics in wireless communication channels, recipient nodes can accurately verify sender node authenticity, bolstering overall communication system security. The proposed method addresses challenges in adapting PLA to dynamic mobile scenarios, particularly dealing with channel variations caused by transmitter mobility. The article evaluates the authentication accuracy a ML-based technique. Overall, this research contributes to advancing wireless network security by integrating ML-based continuous PLA to ensure IoT communication reliability and integrity in dynamic mobile environments.
2024
2024 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0 & IoT)
2024 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0 & IoT)
Marabissi, Dania; Stomaci, Andrea; Mucchi, Lorenzo
File in questo prodotto:
File Dimensione Formato  
Adaptive_Security_in_Mobile_Wireless_Networks_Machine_Learning-Enhanced_Continuous_Physical_Layer_Authentication_for_Dynamic_Environments.pdf

Accesso chiuso

Tipologia: Pdf editoriale (Version of record)
Licenza: Tutti i diritti riservati
Dimensione 1.79 MB
Formato Adobe PDF
1.79 MB Adobe PDF   Richiedi una copia

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

Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1392892
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact