| Reference : Auto-encoding Robot State against Sensor Spoofing Attacks |
| Scientific congresses, symposiums and conference proceedings : Paper published in a journal | |||
| Engineering, computing & technology : Computer science | |||
| Security, Reliability and Trust | |||
| http://hdl.handle.net/10993/41349 | |||
| Auto-encoding Robot State against Sensor Spoofing Attacks | |
| English | |
Rivera, Sean  [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] | |
| Lagraa, Sofiane [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] | |
| Iannillo, Antonio Ken [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] | |
| State, Radu [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >] | |
| Oct-2019 | |
| International Symposium on Software Reliability Engineering | |
| Yes | |
| 4th Intl. Workshop on Reliability and Security Data Analysis | |
| From 28-10-2019 to 31-10-2019 | |
| International Symposium on Software Reliability Engineering | |
| Berlin | |
| Germany | |
| [en] anomaly detection ; robotic systems ; benchmark | |
| [en] In robotic systems, the physical world is highly coupled
with cyberspace. New threats affect cyber-physical systems as they rely on several sensors to perform critical operations. The most sensitive targets are their location systems, where spoofing attacks can force robots to behave incorrectly. In this paper, we propose a novel anomaly detection approach for sensor spoofing attacks, based on an auto-encoder architecture. After initial training, the detection algorithm works directly on the compressed data by computing the reconstruction errors. We focus on spoofing attacks on Light Detection and Ranging (LiDAR) systems. We tested our anomaly detection approach against several types of spoofing attacks comparing four different compression rates for the auto-encoder. Our approach has a 99% True Positive rate and a 10% False Negative rate for the 83% compression rate. However, a compression rate of 41% could handle almost all of the same attacks while using half the data. | |
| ULSNT | |
| CONCORDIA GA 830927. | |
| Researchers | |
| http://hdl.handle.net/10993/41349 | |
| H2020 ; 830927 |
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