Systematic Evaluation of Automotive Intrusion Detection Datasets
Published in ACM Computer Science in Cars Symposium (CSCS) 2022, 2022
Recommended citation: A. Vahidi, T. Rosenstatter, and N. Mowla. 2022. Systematic Evaluation of Automotive Intrusion Detection Datasets. In Computer Science in Cars Symposium (CSCS '22), December 8, 2022, Ingolstadt, Germany. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3568160.3570226
The automotive domain has got its own share of advancements in information and communication technology, providing more services and leading to more connectivity. However, more connectivity and openness raise cyber security and safety concerns. Indeed, services that depend on online connectivity can serve as entry points for attacks on different assets of the vehicle. This study explores collaborative ways of selecting response techniques to counter real-time cyber attacks on automotive systems. The aim is to mitigate the attacks more quickly than a single vehicle would be able to do, and increase the survivability chances of the collaborating vehicles. To achieve that, the design science research methodology is employed. As a result, we present RIPOSTE, a framework for collaborative real-time evaluation and selection of suitable response techniques when an attack is in progress. We evaluate the framework from a safety perspective by conducting a qualitative study involving domain experts. The proposed framework is deemed slightly unsafe, and insights into how to improve the overall safety of the framework are provided.
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The slides can be found here.