Analysis of Autonomous Driving Software to Low-Level Sensor Cyber Attacks
FULL
This program is tentative and subject to change.
Autonomous Vehicle architectures fuse legacy, electro-mechanical components with advanced sensor technology and digital controllers, governed by software. An open challenge for the design of autonomous vehicles are cyber threats such as Electromagnetic Injection (EMI) attacks, to the low-level layer, comprising electro-mechanical components, which can propagate through to the higher-level, intelligent control, affecting decision-making and the safety of the vehicle. This study analyses the robustness of the design of the software stack of a real-world Autonomous Vehicle to EMI attacks on the steering angle sensor. To achieve this, we create a hybrid testbed which combines the mathematical model of the low-level sensor with the high-fidelity, intelligent control. We further develop safety and performance metrics, measured at the high-level, which we use to generate a detailed view on the safety and system performance of the software. We conduct diverse EMI attacks on the target AV, within 3 diverse critical driving scenarios, consisting > 1000 simulations. The results indicate a correlation between an increase in attack noise with an increase in safety violations and failures to complete the mission of the AV. Our results highlight the importance, for AV software developers, of testing under diverse attack and driving scenarios, as each scenario within our experimentation exhibits different behaviour of the system and correlations to differing safety and system performance indicators.
This program is tentative and subject to change.
Tue 29 AprDisplayed time zone: Eastern Time (US & Canada) change
11:00 - 12:30 | |||
11:00 25mTalk | Self-Adaptive Dual-Layer DDoS Mitigation using Autoencoder and Reinforcement LearningFULL Research Track Qi Duan Carnegie Mellon University, Ehab Al-Shaer Carnegie Mellon University, USA, David Garlan Carnegie Mellon University | ||
11:25 25mTalk | Analysis of Autonomous Driving Software to Low-Level Sensor Cyber AttacksFULL Research Track Andrew Roberts Tallinn University of Technology, Mohsen Malayjerdi Tallinn University of Technology, Mauro Bellone FinEst Smart City Centre, Raivo Sell Tallinn University of Technology, Olaf Maennel University of Adelaide, Mohammad Hamad Technical University of Munich, Sebastian Steinhorst Technical University of Munich | ||
11:50 15mTalk | A Comprehensive Analysis of Cybersecurity Challenges in Self-Adaptive Avionics: A Plug&Fly Avionics Platform Case StudySHORT Research Track Aisha Zahid Junejo Universitat Stuttgart, Mario Werthwein Universitat Stuttgart, Bjoern Annighoefer University of Stuttgart | ||
12:05 15mTalk | Towards Using Inductive Learning to Adapt Security Controls in Smart HomesSHORT Research Track Kushal Ramkumar Lero@University College Dublin, Wanling Cai Lero@Trinity College Dublin, John McCarthy Lero@University College Cork, Gavin Doherty Lero@Trinity College Dublin, Bashar Nuseibeh The Open University, UK; Lero, University of Limerick, Ireland, Liliana Pasquale University College Dublin & Lero File Attached | ||
12:20 10mOther | Discussion Session 6 Research Track |