Pepper is a humanoid robot that just embeds the few computational resources for controlling its sensors and actuators, and is not capable of handling big amounts of data or performing in parallel complicate tasks. Aiming at enriching its functionalities and its interaction with the environment, the robot has been put in communication with a plethora of satellite smart objects and services ranging from simple environmental sensors, up to deep learning enhanced smart cameras. The addition of biometric, emotional, social, machine learning and other capabilities to Pepper, while enabling advanced functionalities and additional instruments for controlling users and the environment, raises security and, obviously, privacy concerns. The robot itself, its interaction with the environment and every weakness exposed by the smart objects involved in its eco-system, may represent an exploit point for attacking the smart home and threaten security and privacy. Aiming at preventing attacks and strengthen security, each action with the system is evaluated against the entire context, as detected by the entire eco-system of smart-objects. This paper describes and analyses the experience and how the semantic trust model adopted mitigates the effects of weaknesses and the risks related to smart home cyber-attacks.

中文翻译：

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具有人形机器人防御的上下文信任模型，用于攻击智能生态系统

Pepper 是一种人形机器人，它仅嵌入了少量计算资源来控制其传感器和执行器，无法处理大量数据或并行执行复杂的任务。为了丰富其功能及其与环境的交互，该机器人已与大量卫星智能对象和服务进行通信，从简单的环境传感器到深度学习增强型智能相机。向 Pepper 添加生物识别、情感、社交、机器学习和其他功能，同时启用用于控制用户和环境的高级功能和附加工具，提高了安全性，显然还有隐私问题。机器人本身，它与环境的交互以及其生态系统中涉及的智能对象暴露的每一个弱点，都可能代表攻击智能家居并威胁安全和隐私的利用点。旨在防止攻击和加强安全性，系统的每个动作都根据整个上下文进行评估，由整个智能对象生态系统检测到。本文描述和分析了经验以及采用的语义信任模型如何减轻弱点的影响以及与智能家居网络攻击相关的风险。