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Quantifying trust in autonomous system under uncertainties

机译：不确定条件下量化对自治系统的信任

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摘要

Over the years, autonomous systems have entered almost all the facets of human life. Gradually, higher levels of autonomy are being incorporated into cyber-physical systems (CPS) and Internet-of-things (IoT) devices. However, safety and security has always been a lurking fear behind adoption of autonomous systems such as self-driving vehicles. To address these issues, we develop a framework for quantifying trust in autonomous system. This framework consist of an estimation method, which considers effect of adversarial attacks on sensor measurements. Our estimation algorithm uses a set-membership method during identification of safe states of the system. An important feature of this algorithm is that it can distinguish between adversarial noise and other disturbances. We also verify the autonomous system by first modeling it as networks of priced timed automata (NPTA) with stochastic semantics and then using statistical probabilistic model checking to verify it against probabilistic specifications. The verification process ensures that the autonomous system behave in accordance to safety specifications within a probabilistic threshold. For quantifying trust on the system, we use confidence results provided by the model checking tool. We have demonstrated our approach by using a case study of adaptive cruise control system under sensor spoofing attacks.
机译：多年来，自治系统已经进入了人类生活的几乎所有方面。逐渐地，更高水平的自主权被整合到网络物理系统（CPS）和物联网（IoT）设备中。然而，在采用自动驾驶汽车等自动驾驶系统后，人们始终担心安全隐患。为了解决这些问题，我们开发了一个量化自治系统中信任的框架。该框架由一种估计方法组成，该方法考虑了对抗攻击对传感器测量的影响。我们的估计算法在系统安全状态识别期间使用集合成员方法。该算法的一个重要特征是它可以区分对抗性噪声和其他干扰。我们还通过首先将自治系统建模为具有随机语义的定价定时自动机（NPTA）网络，然后使用统计概率模型检查来针对概率规范对其进行验证，从而验证自治系统。验证过程可确保自治系统按照概率阈值内的安全规范运行。为了量化对系统的信任，我们使用模型检查工具提供的置信度结果。我们通过在传感器欺骗攻击下使用自适应巡航控制系统的案例研究证明了我们的方法。

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