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Towards Artificial-Intelligence-Based Cybersecurity for Robustifying Automated Driving Systems Against Camera Sensor Attacks

Authors: Christos Kyrkou; Andreas Papachristodoulou; Andreas Kloukiniotis; Andreas Papandreou; Aris Lalos; Konstantinos Moustakas; Theocharis Theocharides;

Towards Artificial-Intelligence-Based Cybersecurity for Robustifying Automated Driving Systems Against Camera Sensor Attacks

Abstract

CARAMEL is a European project that aims amongst others to improve and extend cyberthreat detection and mitigation techniques for automotive driving systems. This paper highlights the important role that advanced artificial intelligence and machine learning techniques can have in proactively addressing modern autonomous vehicle cybersecurity challenges and on mitigating associated safety risks when dealing with targetted attacks on a vehicle's camera sensors. The cybersecurity solutions developed by CARAMEL are based on powerful AI tools and algorithms to combat security risks in automated driving systems and will be hosted on embedded processors and platforms. As such, it will be possible to have a specialized anti-hacking device that addresses newly introduced technological dimensions for increased robustness and cybersecurity in addition to industry needs for high speed, low latency, functional safety, light weight, low power consumption.

Keywords

Machine Learning, Neural Networks, Computer Vision, Semantic Segmentation, Robustness, Autonomous Vehicles, Computer Security, Adversarial Learning

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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