Trace-based Timing Analysis of Automotive Software Systems: an Experience Report

Bucaioni, Alessio; Ferko, Enxhi; Lönn, Henrik

doi:10.1109/models-c53483.2021.00046

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…Trace-based analysis is a technique utilized to model the behavior of software components and subsequently identify vulnerabilities that may exist within these components [5]. Within the realm of automotive systems, examining the traces of temporal behavior exhibited by software components across multiple executions can be employed to ascertain whether the software has been compromised in the event of any departure from this established behavior [6]. Nonetheless, this approach does come with limitations: the attacker might succeed in compromising the software component without necessarily surpassing the worst-case execution time of the task, particularly when we consider that the time constraint is often overapproximated [7].…”

Section: Introductionmentioning

confidence: 99%

Creating a Security Enforcement Environment for a Vehicular Platform

Tsantekidis,

Abdelghani,

Hamad

et al. 2023

2023 IEEE Conference on Standards for Communications and Networking (CSCN)

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The ever-increasing complexity of automotive platforms combined with the introduction of commercial off-theshelf software components (e.g., for the entertainment system) creates multiple attack vectors that adversaries can leverage to attack the platform. Traditional analysis techniques have difficulty dealing with such complex environments, especially considering the need for low-cost solutions. Hence, we propose in this paper to turn the logic around, and instead of trying to discover all possible vulnerabilities, we monitor the execution of a software system to ensure that it does not deviate from its nominal profile. In this paper, we demonstrate a technique for creating a state model mapping the execution of a system, and then by observing its interaction with the runtime environment through its invocation of various library functions, we can ensure that offnominal behavior can be detected and acted upon. The valuation results provide further evidence of the wrapper mechanism's effectiveness and highlight its potential to enhance security while minimizing the impact on performance.

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Section: Introductionmentioning

confidence: 99%