Detection of Cyber-attacks to indoor real time localization systems for autonomous robots
Detection of Cyber-attacks to indoor real time localization systems for autonomous robots
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DOI：
10.1016/j.robot.2017.10.006
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发表时间：
2018-01-01
影响因子：
4.3
通讯作者：
Matellan, Vicente
中科院分区：
文献类型：
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作者：
Manuel Guerrero-Higueras, Angel;DeCastro-Garcia, Noemi;Matellan, Vicente
机器人系统的网络安全问题日益受到关注。许多移动机器人在很大程度上依赖于实时定位系统在不同的环境中安全运行。因此，实时定位系统已成为机器人和自主系统的攻击载体，这种情况尚未得到很好的研究。本文表明，实时定位系统的网络攻击可以通过使用监督学习构建的系统来检测。此外，它还表明，对实时定位系统的某些类型的网络攻击，特别是拒绝服务和欺骗，可以通过使用机器学习技术构建的系统检测到。为了构建能够检测这些攻击的模型，使用轮式机器人和基于超宽带信标的商业实时定位系统记录的真实数据集，对不同的监督学习算法进行了测试和验证。交叉验证分析的实验结果表明，多层感知器分类器获得了最高的测试分数和最低的验证误差。此外，该模型对实时定位系统的拒绝服务和欺骗网络攻击检测具有较小的过拟合和较高的灵敏度。(c) 2017 Elsevier B.V.版权所有
Cyber-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. (c) 2017 Elsevier B.V. All rights reserved.