Enhancing Privacy and Security in the Advanced Metering Infrastructure

University dissertation from Chalmers University of Technology

Abstract: Cyberphysical systems introduce computing and communication devices within different sectors of society generating large quantities of data. Information extracted from these data gives better understanding of these sectors and the systems within them. There are many ways in which the information extracted from these data can be harnessed. For example, monitoring these devices is important in order to keep account of their correct functionality, especially in critical infrastructures, and the aforementioned data can be used to construct and enhance such monitoring systems. Processing these data is not always easy, first due to their complexity and also due to the sensitive personal information that might be inferred from them. The processing must be done with care in order to preserve the privacy of the users whose behavior generates the data in question. Different methods and technologies have been proposed in order to preserve the privacy of these per- sonal data and they are tailored to the same data that they are protecting. Because of this, it is important to study the influence of the characteristics of these data on the effectiveness of the privacy preserving solutions employed. The work in this thesis focuses on the Advanced Metering Infrastructure (AMI) in the smart electrical grid and it has two goals. The first one is to study the characteristics of the AMI data and the thesis investigates the effect of these characteristics on a number of privacy enhancing technologies which are proposed for AMI data. The second one is to work towards an Intrusion Detection System (IDS) for the AMI, and the thesis presents an important IDS module which processes encrypted traffic and is able to infer the different commands run between AMI devices, without decrypting the traffic or accessing the sensitive data.