Real-time data operations and causal security analysis for edge-cloud-based Smart Grid infrastructure

Smart Grid;edge computing;causal security analysis;real-time

Ren, Wenyu

关键词: Smart Grid;    edge computing;    causal security analysis;    real-time;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/104828/REN-DISSERTATION-2019.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

The electric power grids are one of the fundamental infrastructures of modern society and are among the most complex networks ever made. Recent development in communications, sensing and measurement techniques has completely changed the traditional electric power grid and has brought us the intelligent electric power grid known as Smart Grid. As a critical cyber-physical system (CPS), Smart Grid is an integration of physical components, sensors, actuators, control centers, and communication networks.The key to orchestrate large scale Smart Grid is to provide situational awareness of the system. And situational awareness is based on large-scale, real-time, accurate collection and analysis of the monitoring and measurement data of the system. However, it is challenging to guarantee situational awareness of Smart Grid. On the one hand, connecting a growing number of heterogeneous programmable devices together introduces new security risks and increases the attack surface of the system. On the other hand, the tremendous amount of measurements from sensors spanning a large geographical area can result in a reduction of available bandwidth and increasing network latency. Both the lack of security protection and the delayed sensor data impede the situational awareness of the system and thus limit the ability to efficiently control and protect large scale Smart Grids in time-critical scenarios.To target the aforementioned challenge, in this thesis, I propose a series of frameworks to provide and guarantee situational awareness in Smart Grid. Taking an integrated approach of edge-cloud design, real-time data operations, and causal security analysis, the proposed frameworks enhance security protection by anomaly detection and managing as well as causal reasoning of alerts, and reduce traffic volume by online data compression. Extensive experiments by real or synthetic traffic demonstrate that the proposed frameworks achieve satisfactory performance and bear great potential practical value.