Towards Privacy-Preserving Local Monitoring and Evaluation of Network Traffic from IoT Devices and Corresponding Mobile Phone Applications

This paper describes ways for users to gain an insight into the actual communication flow of their Internet of Things (IoT) devices. The paper’s main objective is to enable a comparison of the flow with the devices’ intended purpose understandable to the device user. On the basis of what the device sends, the user should be enabled to decide whether the traffic is legitimate or not. With our framework no additional data will leave the user’s premises at any time. Only when a user decides that the traffic is unwanted communication flows the user can voluntarily transfer selected excerpts to a third party for further analysis. This limits data leakage compared to existing security incident event management (SIEM) solutions, where the monitoring third party seeks to constantly collect all information about the user’s traffic and thus constantly gets sensitive information. In this paper we propose a first set of tools for purely local analysis and user-friendly local visualizations. By this we educate the local user/operator of the IoT deployment and allow for more informed and more transparent decisions. Thus, we show that a privacy-preserving and thus more data-protection (GDPR) compliant monitoring of IoT-related network traffic is possible – and showcase how it will look.