Mohammad Esmalifalak

Bio: Mohammad Esmalifalak is an academic researcher from University of Houston. The author has contributed to research in topics: Smart grid & Electric power system. The author has an hindex of 14, co-authored 23 publications receiving 1438 citations. Previous affiliations of Mohammad Esmalifalak include Islamic Azad University & McMaster University.

Detecting False Data Injection Attacks on Power Grid by Sparse Optimization

Abstract: State estimation in electric power grid is vulnerable to false data injection attacks, and diagnosing such kind of malicious attacks has significant impacts on ensuring reliable operations for power systems. In this paper, the false data detection problem is viewed as a matrix separation problem. By noticing the intrinsic low dimensionality of temporal measurements of power grid states as well as the sparse nature of false data injection attacks, a novel false data detection mechanism is proposed based on the separation of nominal power grid states and anomalies. Two methods, the nuclear norm minimization and low rank matrix factorization, are presented to solve this problem. It is shown that proposed methods are able to identify proper power system operation states as well as detect the malicious attacks, even under the situation that collected measurement data is incomplete. Numerical simulation results both on the synthetic and real data validate the effectiveness of the proposed mechanism.

Detecting Stealthy False Data Injection Using Machine Learning in Smart Grid

Abstract: Aging power industries, together with the increase in demand from industrial and residential customers, are the main incentive for policy makers to define a road map to the next-generation power system called the smart grid. In the smart grid, the overall monitoring costs will be decreased, but at the same time, the risk of cyber attacks might be increased. Recently, a new type of attacks (called the stealth attack) has been introduced, which cannot be detected by the traditional bad data detection using state estimation. In this paper, we show how normal operations of power networks can be statistically distinguished from the case under stealthy attacks. We propose two machine-learning-based techniques for stealthy attack detection. The first method utilizes supervised learning over labeled data and trains a distributed support vector machine (SVM). The design of the distributed SVM is based on the alternating direction method of multipliers, which offers provable optimality and convergence rate. The second method requires no training data and detects the deviation in measurements. In both methods, principal component analysis is used to reduce the dimensionality of the data to be processed, which leads to lower computation complexities. The results of the proposed detection methods on IEEE standard test systems demonstrate the effectiveness of both schemes.

Detecting stealthy false data injection using machine learning in smart grid

Abstract: Aging power industries together with increase in the demand from industrial and residential customers are the main incentive for policy makers to define a road map to the next generation power system called smart grid. In smart grid, the overall monitoring costs will be decreased but at the same time, the risk of cyber attacks might be increased. Recently a new type of attacks (called the stealth attack) has been introduced, which cannot be detected by the bad data detection using state estimation. In this paper, we show how normal operations of power networks can be statistically distinguished from the case under stealthy attacks. We devise two machine learning based techniques for stealthy attack detection. The first method utilizes supervised learning over labeled data and trains a support vector machine. The second method requires no training data and detects the deviation in measurement In both methods, principle component analysis is used to reduce the dimensionality of the data to be processed, and thus leads to lower computation complexities. The results of the proposed detection methods on the IEEE standard test systems demonstrate effectiveness of both schemes.

Bad Data Injection Attack and Defense in Electricity Market Using Game Theory Study

Abstract: Applications of cyber technologies improve the quality of monitoring and decision making in smart grid. These cyber technologies are vulnerable to malicious attacks, and compromising them can have serious technical and economical problems. This paper specifies the effect of compromising each measurement on the price of electricity, so that the attacker is able to change the prices in the desired direction (increasing or decreasing). Attacking and defending all measurements are impossible for the attacker and defender, respectively. This situation is modeled as a zero-sum game between the attacker and defender. The game defines the proportion of times that the attacker and defender like to attack and defend different measurements, respectively. From the simulation results based on the PJM 5-Bus test system, we can show the effectiveness and properties of the studied game.

Bad data injection in smart grid: attack and defense mechanisms

Abstract: In modern smart grid networks, the traditional power grid is empowered by technological advances in sensing, measurement, and control devices with two-way communications between the suppliers and consumers. The smart grid integration helps the power grid networks to be smarter, but it also increases the risk of attacks because of the existing obsolete cyber-infrastructure. In this article, we focus on bad data injection attacks for smart grid. The basic problem formulation is presented, and the special type of stealth attack is discussed. Then we investigate the strategies of defenders and attackers, respectively. Specifically, from the defender's perspective, an adaptive cumulative sum test is able to determine the possible existence of adversaries at the control center as quickly as possible. From the attacker's point of view, independent component analysis is employed for the attackers to make inferences through phasor observations without prior knowledge of the power grid topology. The inferred structural information can then be used to launch stealth attacks.

A Review of False Data Injection Attacks Against Modern Power Systems

Abstract: With rapid advances in sensor, computer, and communication networks, modern power systems have become complicated cyber-physical systems. Assessing and enhancing cyber-physical system security is, therefore, of utmost importance for the future electricity grid. In a successful false data injection attack (FDIA), an attacker compromises measurements from grid sensors in such a way that undetected errors are introduced into estimates of state variables such as bus voltage angles and magnitudes. In evading detection by commonly employed residue-based bad data detection tests, FDIAs are capable of severely threatening power system security. Since the first published research on FDIAs in 2009, research into FDIA-based cyber-attacks has been extensive. This paper gives a comprehensive review of state-of-the-art in FDIAs against modern power systems. This paper first summarizes the theoretical basis of FDIAs, and then discusses both the physical and the economic impacts of a successful FDIA. This paper presents the basic defense strategies against FDIAs and discusses some potential future research directions in this field.

Real-Time Detection of False Data Injection Attacks in Smart Grid: A Deep Learning-Based Intelligent Mechanism

Abstract: Application of computing and communications intelligence effectively improves the quality of monitoring and control of smart grids However, the dependence on information technology also increases vulnerability to malicious attacks False data injection (FDI), that attack on the integrity of data, is emerging as a severe threat to the supervisory control and data acquisition system In this paper, we exploit deep learning techniques to recognize the behavior features of FDI attacks with the historical measurement data and employ the captured features to detect the FDI attacks in real-time By doing so, our proposed detection mechanism effectively relaxes the assumptions on the potential attack scenarios and achieves high accuracy Furthermore, we propose an optimization model to characterize the behavior of one type of FDI attack that compromises the limited number of state measurements of the power system for electricity theft We illustrate the performance of the proposed strategy through the simulation by using IEEE 118-bus test system We also evaluate the scalability of our proposed detection mechanism by using IEEE 300-bus test system