English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Nowadays, vehicles have complex in-vehicle networks (IVNs) with millions oflines of code controlling almost every function in the vehicle includingsafety-critical functions. It has recently been shown that IVNs are becomingincreasingly vulnerable to cyber-attacks capable of taking control of vehicles,thereby threatening the safety of the passengers. Several countermeasures havebeen proposed in the literature in response to the arising threats, however,hurdle requirements imposed by the industry is hindering their adoption inpractice. In particular, detecting attacks on IVNs is challenged by strictresource constraints and utterly complex communication patterns that vary evenfor vehicles of the same model. In addition, existing solutions suffer from twomain drawbacks. First, they depend on the underlying vehicle configuration, andsecond, they are incapable of detecting certain attacks of a stealthy nature.In this paper, we propose CASAD, a CAN-Aware Stealthy-Attack Detectionmechanism that does not abide by the strict specifications predefined for everyvehicle model and addresses key real-world deployability challenges. Our fast,lightweight, and system-agnostic approach learns the normal behavior of IVNdynamics from historical data and detects deviations by continuously monitoringIVN traffic. We demonstrate the effectiveness of CASAD by conducting variousexperiments on a CAN bus prototype, a 2018 Volvo XC60, and publicly availabledata from two real vehicles. Our approach is experimentally shown to beeffective against different attack scenarios, including the prompt detection ofstealthy attacks, and has considerable potential applicability to realvehicles.

CASAD: CAN-Aware Stealthy-Attack Detection for In-Vehicle Networks