Detection of Cyber-attacks to indoor real time localization systems for autonomous robots

摘要

AbstractCyber-security for robotic systems is a growing concern. Many mobile robots rely heavily on Real Time Location Systems to operate safely in different environments. As a result, Real Time Location Systems have become a vector of attack for robots and autonomous systems, a situation which has not been studied well. This article shows that cyber-attacks on Real Time Location Systems can be detected by a system built using supervised learning. Furthermore it shows that some type of cyber-attacks on Real Time Location Systems, specifically Denial of Service and Spoofing, can be detected by a system built using Machine Learning techniques. In order to construct models capable of detecting those attacks, different supervised learning algorithms have been tested and validated using a dataset of real data recorded by a wheeled robot and a commercial Real Time Location System, based on Ultra Wideband beacons. Experimental results with a cross-validation analysis have shown that Multi-Layer Perceptron classifiers get the highest test score and the lowest validation error. Moreover, it is the model with less overfitting and more sensitivity for detecting Denial of Service and Spoofing cyber-attacks on Real Time Location Systems.Highlights?A method to build models to detect cyber-attacks on RTLSs is proposed.?Some type of cyber-attacks on RTLSs can be detected by a using ML techniques.?Eight well-known classifiers and predictor algorithms have been evaluated.?Cross-validation analysis have shown that MLP classifiers works better than others.]]>