This paper proposes a novel intrusion detection algorithm that aims to identify malicious CAN messages injected by attackers in the CAN bus of modern vehicles. The proposed algorithm identifies anomalies in the sequence of messages that flow in the CAN bus and is characterized by small memory and computational footprints, that make it applicable to current ECUs. Its detection performance are demonstrated through experiments carried out on real CAN traffic gathered from an unmodified licensed vehicle.

Anomaly detection of CAN bus messages through analysis of ID sequences / Marchetti, Mirco; Stabili, Dario. - (2017), pp. 1577-1583. (Intervento presentato al convegno 28th IEEE Intelligent Vehicles Symposium, IV 2017 tenutosi a usa nel 2017) [10.1109/IVS.2017.7995934].

Anomaly detection of CAN bus messages through analysis of ID sequences

Marchetti, Mirco
;
Stabili, Dario
2017

Abstract

This paper proposes a novel intrusion detection algorithm that aims to identify malicious CAN messages injected by attackers in the CAN bus of modern vehicles. The proposed algorithm identifies anomalies in the sequence of messages that flow in the CAN bus and is characterized by small memory and computational footprints, that make it applicable to current ECUs. Its detection performance are demonstrated through experiments carried out on real CAN traffic gathered from an unmodified licensed vehicle.
2017
28th IEEE Intelligent Vehicles Symposium, IV 2017
usa
2017
1577
1583
Marchetti, Mirco; Stabili, Dario
Anomaly detection of CAN bus messages through analysis of ID sequences / Marchetti, Mirco; Stabili, Dario. - (2017), pp. 1577-1583. (Intervento presentato al convegno 28th IEEE Intelligent Vehicles Symposium, IV 2017 tenutosi a usa nel 2017) [10.1109/IVS.2017.7995934].
File in questo prodotto:
File Dimensione Formato  
FINAL_SUBMISSION.pdf

Open access

Descrizione: Articolo principale
Tipologia: Versione dell'autore revisionata e accettata per la pubblicazione
Dimensione 399.3 kB
Formato Adobe PDF
399.3 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1149168
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 168
  • ???jsp.display-item.citation.isi??? 120
social impact