Titlebook: Computer Safety, Reliability, and Security; 40th International C Ibrahim Habli,Mark Sujan,Friedemann Bitsch Conference proceedings 2021 Spr

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The Beecham Manual for Family Practiceing vehicle operation has not yet received much attention. In this work, we describe a context-aware scheme for automated road vehicles that assesses the risk of security incidents intending to automatically identify adequate countermeasures. We specifically focus on attack propagation, as related w

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gene-therapy

The Beecham Manual for Family PracticeLs). Products within an SPL vary from each other in terms of which features they include. Applying existing analysis techniques to SPLs and their safety cases is usually challenging because of the potentially exponential number of products with respect to the number of supported features. In this pa

Interstellar

The Beecham Manual for Family Practice method applied during the assurance process is described including the sources of evidence and deviations from previous ISO 26262 based approaches. The paper highlights how the choice of machine learning approach supports the assurance case, especially regarding the inherent explainability of the a

HILAR

The Beecham Manual for Family Practicecially, blind corners pose a critical scenario, in which infrastructure-based sensors can provide more safety. The automation of vehicles is usually tied to an argument on improved safety. However, current standards still rely on the awareness of humans to avoid collisions, which is limited at corne

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The Beecham Manual for Family Practicecal components. This work attempts to ease this labour-intensive process by using machine learning-based fault injection to discover representative hazardous situations. Using a Simulation-Aided Hazard Analysis and Risk Assessment (SAHARA) methodology, a visualisation and suggested hazard classifica

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atopic-rhinitis

https://doi.org/10.1007/978-94-009-7334-3s. However, the inclusion of trained agents in automotive systems exposes the vehicle to novel attacks and faults, that can result in safety threats to the driving tasks. In this paper we report our experimental campaign on the injection of adversarial attacks and software faults in a self-driving a

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