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Quantifying trust in autonomous system under uncertainties

机译：在不确定因素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所有方面。逐步地，较高水平的自主权被纳入网络 - 物理系统（CPS）和内部互联网（物联网）设备。然而，安全和安全始终是采用自动驾驶车辆的自治系统背后潜伏的恐惧。为解决这些问题，我们开发了一种用于量化自治系统信任的框架。该框架包括一种估计方法，其考虑对抗对传感器测量的对抗攻击的影响。我们的估计算法在识别系统安全状态期间使用设定成员资格方法。该算法的一个重要特征是它可以区分对抗噪声和其他干扰。我们还通过将其作为具有随机语义的价格为定时自动机（NPTA）的网络，然后使用统计概率模型检查来验证自主系统，然后使用统计概率模型检查来验证概率规格。验证过程可确保自治系统根据概率阈值的安全规范行事。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。我们通过使用传感器欺骗攻击下的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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