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Abstract
Autonomous systems are emerging in many application domains. With the recent
advancements in artificial intelligence and machine learning, sensor
technology, perception algorithms and robotics, scenarios previously requiring
strong human involvement can be handled by autonomous systems. With the
independence from human control, cybersecurity of such systems becomes even
more critical as no human intervention in case of undesired behavior is
possible. In this context, this paper discusses emerging security challenges in
autonomous systems design which arise in many domains such as autonomous
incident response, risk assessment, data availability, systems interaction,
trustworthiness, updatability, access control, as well as the reliability and
explainability of machine learning methods. In all these areas, this paper
thoroughly discusses the state of the art, identifies emerging security
challenges and proposes research directions to address these challenges for
developing secure autonomous systems.
External Datasets
Apricot: A dataset of physical adversarial attacks on object detection
Simulating malicious attacks on vanets for connected and autonomous vehicle cybersecurity: A machine learning dataset
Attack data generation framework for autonomous vehicle sensors