Graphical Convolution Network Based Semi-Supervised Methods for Detecting PMU Data Manipulation Attacks

With the integration of information and communications technologies (ICTs) into the power grid, electricity infrastructures are gradually transformed towards smart grid and power systems become more open to and accessible from outside networks. With ubiquitous sensors, computers and communication networks, modern power systems have become complicated cyber-physical systems. The cyber security issues and the impact of potential attacks on the smart grid have become an important issue. Among these attacks, false data injection attack (FDIA) becomes a growing concern because of its varied types and impacts. Several detection algorithms have been developed in the last few years, which were model-based, trajectory prediction-based or learning-based methods. Phasor measurement units (PMUs) and supervisory control and data acquisition (SCADA) system work together to monitor the power system operation. The unsecured devices could offer opportunities to adversaries to compromise the system. In the literature review part of this thesis, the main methods are compared considering computing accuracy and complexity. Most work about PMUs ignored the reality that the number of PMUs installed in a power system is limited to realize observability because of high installing cost. Therefore, based on observable truth of PMU and the topology structure of power system, the graph convolution network (GCN) is proposed in this thesis. The main idea is using selected features to define violated PMU, and GCN is used to classify susceptible violated nodes and normal nodes. The basic detection method is introduced at first. And then the calculation process of neural network and Fourier transform are described with more details about graph convolution network. Later, the proposed detection mechanism and algorithm are introduced. Finally, the simulation results are given and analyzed

Outlier Identification in Spatio-Temporal Processes

This dissertation answers some of the statistical challenges arising in spatio-temporal data from Internet traffic, electricity grids and climate models. It begins with methodological contributions to the problem of anomaly detection in communication networks. Using electricity consumption patterns for University of Michigan campus, the well known spatial prediction method kriging has been adapted for identification of false data injections into the system. Events like Distributed Denial of Service (DDoS), Botnet/Malware attacks, Port Scanning etc. call for methods which can identify unusual activity in Internet traffic patterns. Storing information on the entire network though feasible cannot be done at the time scale at which data arrives. In this work, hashing techniques which can produce summary statistics for the network have been used. The hashed data so obtained indeed preserves the heavy tailed nature of traffic payloads, thereby providing a platform for the application of extreme value theory (EVT) to identify heavy hitters in volumetric attacks. These methods based on EVT require the estimation of the tail index of a heavy tailed distribution. The traditional estimators (Hill et al. (1975)) for the tail index tend to be biased in the presence of outliers. To circumvent this issue, a trimmed version of the classic Hill estimator has been proposed and studied from a theoretical perspective. For the Pareto domain of attraction, the optimality and asymptotic normality of the estimator has been established. Additionally, a data driven strategy to detect the number of extreme outliers in heavy tailed data has also been presented. The dissertation concludes with the statistical formulation of m-year return levels of extreme climatic events (heat/cold waves). The Generalized Pareto distribution (GPD) serves as good fit for modeling peaks over threshold of a distribution. Allowing the parameters of the GPD to vary as a function of covariates such as time of the year, El-Nino and location in the US, extremes of the areal impact of heat waves have been well modeled and inferred.PHDStatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145789/1/shrijita_1.pd