Although vehicle platooning promises to improve transportation efficiency and safety by leveraging communication between convoy members, preliminary results in previous work suggest that cyber-attacks could deceive many Cooperative Adaptive Cruise Control algorithms, hence endangering the safety of every participant. This paper deeply analyzes the case of injection attacks. First, we introduce an extensive security analysis carried out through realistic simulations, to demonstrate how even slight and smooth falsification attacks do succeed in fooling the CACC controllers and cause numerous vehicle crashes. Second, we present a novel misbehavior detection technique. It leverages the correlation between multiple motion parameters concerning both single and consecutive vehicles to evaluate the plausibility of the information received from the other members. Extensive validation confirms the effectiveness of the technique proposed: overall, it succeeds to detect all the attacks simulated and prevents the occurrence of safety-critical situations.

Detecting Injection Attacks on Cooperative Adaptive Cruise Control / Iorio, Marco; Risso, FULVIO GIOVANNI OTTAVIO; Sisto, Riccardo; Buttiglieri, Alberto; Reineri, Massimo. - ELETTRONICO. - (2019), pp. 75-82. ((Intervento presentato al convegno 2019 IEEE Vehicular Networking Conference (VNC) (IEEE VNC 2019) tenutosi a Los Angeles (USA) nel 4-6 Dicembre 2019 [10.1109/VNC48660.2019.9062798].

Detecting Injection Attacks on Cooperative Adaptive Cruise Control

Marco Iorio;Fulvio Risso;Riccardo Sisto;
2019

Abstract

Although vehicle platooning promises to improve transportation efficiency and safety by leveraging communication between convoy members, preliminary results in previous work suggest that cyber-attacks could deceive many Cooperative Adaptive Cruise Control algorithms, hence endangering the safety of every participant. This paper deeply analyzes the case of injection attacks. First, we introduce an extensive security analysis carried out through realistic simulations, to demonstrate how even slight and smooth falsification attacks do succeed in fooling the CACC controllers and cause numerous vehicle crashes. Second, we present a novel misbehavior detection technique. It leverages the correlation between multiple motion parameters concerning both single and consecutive vehicles to evaluate the plausibility of the information received from the other members. Extensive validation confirms the effectiveness of the technique proposed: overall, it succeeds to detect all the attacks simulated and prevents the occurrence of safety-critical situations.
978-1-7281-4571-6
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2784632