A Stealth, Selective, Link-Layer Denial-of-Service Attack Against Automotive Networks

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10327)


Modern vehicles incorporate tens of electronic control units (ECUs), driven by as much as 100,000,000 lines of code. They are tightly interconnected via internal networks, mostly based on the CAN bus standard. Past research showed that, by obtaining physical access to the network or by remotely compromising a vulnerable ECU, an attacker could control even safety-critical inputs such as throttle, steering or brakes. In order to secure current CAN networks from cyberattacks, detection and prevention approaches based on the analysis of transmitted frames have been proposed, and are generally considered the most time- and cost-effective solution, to the point that companies have started promoting aftermarket products for existing vehicles.

In this paper, we present a selective denial-of-service attack against the CAN standard which does not involve the transmission of any complete frames for its execution, and thus would be undetectable via frame-level analysis. As the attack is based on CAN protocol weaknesses, all CAN bus implementations by all manufacturers are vulnerable. In order to precisely investigate the time, money and expertise needed, we implement an experimental proof-of-concept against a modern, unmodified vehicle and prove that the barrier to entry is extremely low. Finally, we present a discussion of our threat analysis, and propose possible countermeasures for detecting and preventing such an attack.


Intrusion Detection Controller Area Network Target Vehicle Target Frame Wire Harness 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Dipartimento di Elettronica, Informazione e BioingegneriaPolitecnico di MilanoMilanItaly
  2. 2.Linklayer LabsTorontoCanada
  3. 3.FTRTrend Micro, Inc.MilanItaly

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