Characterization of Model-Based Detectors for CPS Sensor Faults/Attacks

A vector-valued model-based cumulative sum (CUSUM) procedure is proposed for identifying faulty/falsified sensor measurements. First, given the system dynamics, we derive tools for tuning the CUSUM procedure in the fault/attack free case to fulfill a desired detection performance (in terms of false alarm rate). We use the widely-used chi-squared fault/attack detection procedure as a benchmark to compare the performance of the CUSUM. In particular, we characterize the state degradation that a class of attacks can induce to the system while enforcing that the detectors (CUSUM and chi-squared) do not raise alarms. In doing so, we find the upper bound of state degradation that is possible by an undetected attacker. We quantify the advantage of using a dynamic detector (CUSUM), which leverages the history of the state, over a static detector (chi-squared) which uses a single measurement at a time. Simulations of a chemical reactor with heat exchanger are presented to illustrate the performance of our tools.Comment: Submitted to IEEE Transactions on Control Systems Technolog

Secure Distributed Dynamic State Estimation in Wide-Area Smart Grids

Smart grid is a large complex network with a myriad of vulnerabilities, usually operated in adversarial settings and regulated based on estimated system states. In this study, we propose a novel highly secure distributed dynamic state estimation mechanism for wide-area (multi-area) smart grids, composed of geographically separated subregions, each supervised by a local control center. We firstly propose a distributed state estimator assuming regular system operation, that achieves near-optimal performance based on the local Kalman filters and with the exchange of necessary information between local centers. To enhance the security, we further propose to (i) protect the network database and the network communication channels against attacks and data manipulations via a blockchain (BC)-based system design, where the BC operates on the peer-to-peer network of local centers, (ii) locally detect the measurement anomalies in real-time to eliminate their effects on the state estimation process, and (iii) detect misbehaving (hacked/faulty) local centers in real-time via a distributed trust management scheme over the network. We provide theoretical guarantees regarding the false alarm rates of the proposed detection schemes, where the false alarms can be easily controlled. Numerical studies illustrate that the proposed mechanism offers reliable state estimation under regular system operation, timely and accurate detection of anomalies, and good state recovery performance in case of anomalies

Tuning Windowed Chi-Squared Detectors for Sensor Attacks

A model-based windowed chi-squared procedure is proposed for identifying falsified sensor measurements. We employ the widely-used static chi-squared and the dynamic cumulative sum (CUSUM) fault/attack detection procedures as benchmarks to compare the performance of the windowed chi-squared detector. In particular, we characterize the state degradation that a class of attacks can induce to the system while enforcing that the detectors do not raise alarms (zero-alarm attacks). We quantify the advantage of using dynamic detectors (windowed chi-squared and CUSUM detectors), which leverages the history of the state, over a static detector (chi-squared) which uses a single measurement at a time. Simulations using a chemical reactor are presented to illustrate the performance of our tools

A Study for Detection of Drift in Sensor Measurements

This study aims to develop methods for detection of drift in sensor measurements. The study consists of three major components; 1) residual generation, 2) statistical change detection, and 3) model building. To identify the statistical properties of the residuals and to utilize them for detection of the drift, a new method for estimation of the drift rate is proposed. The method formulates an augmented system matrix model and processes the model using a Kalman filter. An analytical method for estimation of the drift rate is also derived. A Hamiltonian approach is used for evaluation of the steady state covariance of the residuals. The steady state covariance and the estimated drift rate enable the existence of the drift in the measurements to be determined in a statistical way using the change detection algorithms. The statistical change detection algorithms process the residuals to determine the drift statistically. In the study, performance of the major algorithms, including the Exponentially Weighted Moving average (EWMA), Cumulative Sum (CUSUM) control chart, and Generalized Likelihood Ratio Test (GRLT), are investigated. A new method for detection of the change, named the Standardized Sum of the Innovation Test (SSIT), is also proposed. The statistical properties of the decision function of the SSIT are derived to set the decision threshold statistically. A method for estimation of the mean delay of the SSIT is also derived. The mean delay of the SSIT is shown in a demonstration and is the shortest of the change detection algorithms. For demonstration purposes, mathematical models of a pressurizer in a CANada Deuterium Uranium (CANDU) nuclear power plant are developed. The mathematical models in the form of nonlinear differential equations are verified by comparing the simulation results with those of the industry standard code known as CATHENA (Canadian Algorithm for Thermal Hydraulic Network Analysis). The developed algorithms have been successfully applied to the pressurizer model for detection and estimation of pressure sensor drifts. The results convincingly demonstrate the effectiveness of the proposed algorithms in the detection of the drift