Omar Ali Beg

Bio: Omar Ali Beg is an academic researcher from University of Texas of the Permian Basin. The author has contributed to research in topics: Microgrid & Cyber-physical system. The author has an hindex of 6, co-authored 13 publications receiving 242 citations. Previous affiliations of Omar Ali Beg include University of Texas at Arlington & University of Texas at San Antonio.

Detection of False-Data Injection Attacks in Cyber-Physical DC Microgrids

Abstract: Power electronics-intensive dc microgrids use increasingly complex software-based controllers and communication networks. They are evolving into cyber-physical systems (CPS) with sophisticated interactions between physical and computational processes, making them vulnerable to cyber attacks. This paper presents a framework to detect possible false-data injection attacks (FDIAs) in cyber-physical dc microgrids. The detection problem is formalized as identifying a change in sets of inferred candidate invariants. Invariants are microgrids properties that do not change over time. Both the physical plant and the software controller of CPS can be described as Simulink/Stateflow (SLSF) diagrams. The dynamic analysis infers the candidate invariants over the input/output variables of SLSF components. The reachability analysis generates the sets of reachable states (reach sets) for the CPS modeled as hybrid automata. The candidate invariants that contain the reach sets are called the actual invariants. The candidate invariants are then compared with the actual invariants, and any mismatch indicates the presence of FDIA. To evaluate the proposed methodology, the hybrid automaton of a dc microgrid, with a distributed cooperative control scheme, is presented. The reachability analysis is performed to obtain the reach sets and, hence, the actual invariants. Moreover, a prototype tool, HYbrid iNvariant GEneratoR, is extended to instrument SLSF models, obtain candidate invariants, and identify FDIA.

Signal Temporal Logic-Based Attack Detection in DC Microgrids

Abstract: Emerging converter-dominated dc microgrids employ distributed cooperative control strategies and communication network. Since there is no central entity to monitor and assess the global cyber scenario, microgrids employing distributed control are prone to cyber attacks. This work presents signal temporal logic (STL) detection of two major types of cyber attacks, namely false-data injection attacks and denial-of-service attacks. Such cyber attacks can compromise voltage regulation and load sharing in dc microgrids. STL is a formalism to monitor the output voltages and currents of dc microgrids against the defined specifications, such as operational bounds, over time. Besides detection, the proposed approach also quantifies the attack impact. Moreover, it can be effectively employed for a complex dc microgrid without prior knowledge of its dynamics. This detection technique is successfully demonstrated using a physical microgrid setup or in a hardware-in-the-loop environment, where various attacks are formalized, detected, and quantified.

Model Validation of PWM DC–DC Converters

Abstract: This paper presents hybrid automaton modeling, comparative model validation, and formal verification of stability through reachability analysis of pulse width modulation (PWM) dc–dc converters. Conformance degree provides a measure of closeness between the proposed hybrid automata models and experimental data. Nondeterminism due to variations in circuit parameters is modeled using interval matrices. In direct contrast to the unsound and computationally-intensive Monte Carlo simulation, reachability analysis is introduced to overapproximate the set of reachable states and ensure stable operation of PWM dc–dc converters. Using a 200 W experimental prototype of a buck converter, hybrid automata models of open-loop, and hysteresis-controlled converters are first validated against experimental data using their conformance degrees. Next, converter stability is formally verified through reachability analysis and informally validated using Monte Carlo simulations and experimental results.

Resilient Networked AC Microgrids Under Unbounded Cyber Attacks

Abstract: This paper considers a cooperative and adversarial AC microgrid system consisting of cooperative leaders and inverters, as well as adversarial attackers. The attackers aim to destabilize the synchronization dynamics of the AC microgrid by first intercepting the communication channels, penetrating the local state feedback, and pretending to be a cooperative neighbor, and then initiating malicious attacks by launching unbounded injections. A fully distributed resilient control framework is offered for the secondary frequency regulation and voltage containment to ensure system stability and preserve bounded synchronization. In particular, a virtual resilient layer with hidden networks is developed to integrate with the original cyber-physical layer. The proposed resilient control framework is fully distributed without requiring any global information. A modified IEEE 34-bus test feeder benchmark system is emulated in a controller/hardware-in-the-loop environment, where the control objectives are met under different attack scenarios.

A Survey on the Detection Algorithms for False Data Injection Attacks in Smart Grids

Abstract: Cyber-physical attacks are the main substantial threats facing the utilization and development of the various smart grid technologies. Among these attacks, false data injection attack represents a main category with its widely varied types and impacts that have been extensively reported recently. In addressing this threat, several detection algorithms have been developed in the last few years. These were either model-based or data-driven algorithms. This paper provides an intensive summary of these algorithms by categorizing them and elaborating on the pros and cons of each category. The paper starts by introducing the various cyber-physical attacks along with the main reported incidents in history. The significance and the impacts of the false data injection attacks are then reported. The concluding remarks present the main criteria that should be considered in developing future detection algorithms for the false data injection attacks.

Secure State Estimation and Control of Cyber-Physical Systems: A Survey

Abstract: Cyber-physical systems (CPSs) empower the integration of physical processes and cyber infrastructure with the aid of ubiquitous computation resources and communication capabilities. CPSs have permeated modern society and found extensive applications in a wide variety of areas, including energy, transportation, advanced manufacturing, and medical health. The security of CPSs against cyberattacks has been regarded as a long-standing concern. However, CPSs suffer from extendable vulnerabilities that are beyond classical networked systems due to the tight integration of cyber and physical components. Sophisticated and malicious cyberattacks continue to emerge to adversely impact CPS operation, resulting in performance degradation, service interruption, and system failure. Secure state estimation and control technologies play a vital role in warranting reliable monitoring and operation of safety-critical CPSs. This article provides a review of the state-of-the-art results for secure state estimation and control of CPSs. Specifically, the latest development of secure state estimation is summarized in light of different performance indicators and defense strategies. Then, the recent results on secure control are discussed and classified into three categories: 1) centralized secure control; 2) distributed secure control; and 3) resource-aware secure control. Furthermore, two specific application examples of water supply distribution systems and wide-area power systems are presented to demonstrate the applicability of secure state estimation and control approaches. Finally, several challenging issues are discussed to direct future research.

Online False Data Injection Attack Detection With Wavelet Transform and Deep Neural Networks

Abstract: State estimation is critical to the operation and control of modern power systems. However, many cyber-attacks, such as false data injection attacks, can circumvent conventional detection methods and interfere the normal operation of grids. While there exists research focusing on detecting such attacks in dc state estimation, attack detection in ac systems is also critical, since ac state estimation is more widely employed in power utilities. In this paper, we propose a new false data injection attack detection mechanism for ac state estimation. When malicious data are injected in the state vectors, their spatial and temporal data correlations may deviate from those in normal operating conditions. The proposed mechanism can effectively capture such inconsistency by analyzing temporally consecutive estimated system states using wavelet transform and deep neural network techniques. We assess the performance of the proposed mechanism with comprehensive case studies on IEEE 118- and 300-bus power systems. The results indicate that the mechanism can achieve a satisfactory attack detection accuracy. Furthermore, we conduct a preliminary sensitivity test on the control parameters of the proposed mechanism.

Artificial Intelligence for Detection, Estimation, and Compensation of Malicious Attacks in Nonlinear Cyber-Physical Systems and Industrial IoT

Abstract: This article proposes a hybrid intelligent-classic control approach for reconstruction and compensation of cyber attacks launched on inputs of nonlinear cyber-physical systems (CPS) and industrial Internet of Things systems, which work through shared communication networks. In this article, a class of n -order nonlinear systems is considered as a model of CPS while it is in presence of cyber attacks only in the forward channel. An intelligent-classic control system is developed to compensate cyber-attacks. Neural network (NN) is designed as an intelligent estimator for attack estimation and a classic nonlinear control system based on the variable structure control method is designed to compensate the effect of attacks and control the system performance in tracking applications. In the proposed strategy, nonlinear control theory is applied to guarantee the stability of the system when attacks happen. In this strategy, a Gaussian radial basis function NN is used for online estimation and reconstruction of cyber-attacks launched on the networked system. An adaptation law of the intelligent estimator is derived from a Lyapunov function. Simulation results demonstrate the validity and feasibility of the proposed strategy in car cruise control application as the testbed.

Deep Learning-Based Interval State Estimation of AC Smart Grids Against Sparse Cyber Attacks

Abstract: Due to the aging of electric infrastructures, conventional power grid is being modernized toward smart grid that enables two-way communications between consumer and utility, and thus more vulnerable to cyber-attacks However, due to the attacking cost, the attack strategy may vary a lot from one operation scenario to another from the perspective of adversary, which is not considered in previous studies Therefore, in this paper, scenario-based two-stage sparse cyber-attack models for smart grid with complete and incomplete network information are proposed Then, in order to effectively detect the established cyber-attacks, an interval state estimation-based defense mechanism is developed innovatively In this mechanism, the lower and upper bounds of each state variable are modeled as a dual optimization problem that aims to maximize the variation intervals of the system variable At last, a typical deep learning, ie, stacked auto-encoder, is designed to properly extract the nonlinear and nonstationary features in electric load data These features are then applied to improve the accuracy for electric load forecasting, resulting in a more narrow width of state variables The uncertainty with respect to forecasting errors is modeled as a parametric Gaussian distribution The validation of the proposed cyber-attack models and defense mechanism have been demonstrated via comprehensive tests on various IEEE benchmarks