A Multi-Observer Based Estimation Framework for Nonlinear Systems under Sensor Attacks

We address the problem of state estimation and attack isolation for general discrete-time nonlinear systems when sensors are corrupted by (potentially unbounded) attack signals. For a large class of nonlinear plants and observers, we provide a general estimation scheme, built around the idea of sensor redundancy and multi-observer, capable of reconstructing the system state in spite of sensor attacks and noise. This scheme has been proposed by others for linear systems/observers and here we propose a unifying framework for a much larger class of nonlinear systems/observers. Using the proposed estimator, we provide an isolation algorithm to pinpoint attacks on sensors during sliding time windows. Simulation results are presented to illustrate the performance of our tools.Comment: arXiv admin note: text overlap with arXiv:1806.0648

A secure state estimation algorithm for nonlinear systems under sensor attacks

The state estimation of continuous-time nonlinear systems in which a subset of sensor outputs can be maliciously controlled through injecting a potentially unbounded additive signal is considered in this paper. Analogous to our earlier work for continuous-time linear systems in \cite{chong2015observability}, we term the convergence of the estimates to the true states in the presence of sensor attacks as `observability under $M$ attacks', where $M$ refers to the number of sensors which the attacker has access to. Unlike the linear case, we only provide a sufficient condition such that a nonlinear system is observable under $M$ attacks. The condition requires the existence of asymptotic observers which are robust with respect to the attack signals in an input-to-state stable sense. We show that an algorithm to choose a compatible state estimate from the state estimates generated by the bank of observers achieves asymptotic state reconstruction. We also provide a constructive method for a class of nonlinear systems to design state observers which have the desirable robustness property. The relevance of this study is illustrated on monitoring the safe operation of a power distribution network.Comment: This paper has been accepted for publication at the 59th IEEE Conference on Decision and Control, 202

A Robust CACC Scheme Against Cyberattacks Via Multiple Vehicle-to-Vehicle Networks

Cooperative Adaptive Cruise Control (CACC) is a vehicular technology that allows groups of vehicles on the highway to form in closely-coupled automated platoons to increase highway capacity and safety, and decrease fuel consumption and CO2 emissions. The underlying mechanism behind CACC is the use of Vehicle-to-Vehicle (V2V) wireless communication networks to transmit acceleration commands to adjacent vehicles in the platoon. However, the use of V2V networks leads to increased vulnerabilities against faults and cyberattacks at the communication channels. Communication networks serve as new access points for malicious agents trying to deteriorate the platooning performance or even cause crashes. Here, we address the problem of increasing robustness of CACC schemes against cyberattacks by the use of multiple V2V networks and a data fusion algorithm. The idea is to transmit acceleration commands multiple times through different communication networks (channels) to create redundancy at the receiver side. We exploit this redundancy to obtain attack-free estimates of acceleration commands. To accomplish this, we propose a data-fusion algorithm that takes data from all channels, returns an estimate of the true acceleration command, and isolates compromised channels. Note, however, that using estimated data for control introduces uncertainty into the loop and thus decreases performance. To minimize performance degradation, we propose a robust $H_{\infty}$ controller that reduces the joint effect of estimation errors and sensor/channel noise in the platooning performance (tracking performance and string stability). We present simulation results to illustrate the performance of our approach

Measurements, Models, Systems and Design

531 s.

New Approaches to Smart Grid Security with SCADA Systems

The use of information technology in electric power grid introduces the vulnerability problem looming the future smart grid. The supervisory control and data acquisition (SCADA)is the first defense, which itself is undermined by potential malicious attacks. This dissertation studies two particular security threats facing the smart grid and SCADA systems: the unobservable attack and the replay attack. The former is well known in fault detection of the power grid and has received renewed interest in the past a few years, while the latter is motivated by the Stuxnet worm allegedly used against the nuclear facilities in Iran. For unobservable attacks, this dissertation adopts the dynamic state estimation approach and treats each bus of the power grid as a dynamic agent. A consensus estimation strategy is proposed to estimate the dynamic states of the power grid, based on which unobservable attacks can be effectively detected. Detection of replay attacks is harder. Two different approaches are proposed in this dissertation. The first is the whitening filter approach that converts the detection of the replay attack into an equivalent white noise detection through whitening a feedback signal. However this approach is less effective, if the replay attack does not change much the whiteness of the filtered feedback signal. Hence a second approach termed as spectrum estimation is proposed. It is shown that the spectrum of the feedback signal in presence of the replay attack can be very different from the case when the replay attack is absent. This approach improves the detection results of the former one. Both are illustrated and examined by the simulation studies

Advances in Intelligent Vehicle Control

This book is a printed edition of the Special Issue Advances in Intelligent Vehicle Control that was published in the journal Sensors. It presents a collection of eleven papers that covers a range of topics, such as the development of intelligent control algorithms for active safety systems, smart sensors, and intelligent and efficient driving. The contributions presented in these papers can serve as useful tools for researchers who are interested in new vehicle technology and in the improvement of vehicle control systems

Sensor Intrusion Detection in Control Systems Using Estimation Theory

In this thesis, two different approaches to sensor intrusion detection are presented. In the first approach, an estimation algorithm using a bank of Kalman Filters is designed that is capable of estimating the intrusion signal when sensors are affected in control systems. The mathematical models of the control system will be estabilished and the system measurement will be shown and after that, various false signals, such as constant-type and ramp-type signal, will be selected as the intrusion signal to affect the system output mentioned above. The system measurement will be tested based on a bank of Kalman Filters. The probabilities of each intrusion state (affected and unaffected) of the control system will be calculated as a function of time. The estimation of the states from a bank of Kalman Filters together with the associated probabilities will determine whether the sensor is under attack or not by using the information from the estimation algorithm. The performance of the algorithm will be tested based on the various levels of the system and measurement noise.In the second approach, a new estimation algorithm is applied to detect the intrusion signal targeting the system mentioned above. By calculating the sample mean value of the system state and measurement in time, the changes of the system measurement can be detected by calculating the residual between the actual value and the theoretical sample mean value of the system measurement and in that case, the intrusion signal can be found. Thesis conclusions, summary and future work is also mentioned in the last chapter of this work