Detecting Cyber-attacks to Smart Grids and Increasing Resiliency Using Data Driven Algorithms



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Data driven algorithms can be generally divided into two main categories including optimization methods and machine learning approaches. Optimization methods try to find the optimal decision states by finding the feasible boundaries of the problem. On the other hand, machine learning algorithms aim to find the solutions by iterating via small steps toward the optimal answer following the gradient descents. These two data-driven algorithms are widely deployed in many science and engineering fields and in this dissertation, we use both of these methods to address cyber-security issues of smart grids. We first use the optimization algorithm to present two bi-level problems to address the bidding problem in electricity markets and cyber-attack detection in virtual bidding process in electricity markets. We investigate False Data Injection (FDI) problem in smart grids and the approaches the detect attacks. Both models are solved using mathematical programming with equality constraint (MPEC) and the possible cyber-attack's locations and malicious data are identified. We then study the machine learning abilities to learn the cyber-attacker's behavior using real data. We use the Day-ahead (DA) and Real-time (RT) electricity price and demand to create our initial model of the cyber-attacker. Then, we apply a zero-sum game between the cyber-attacker and system defender using novel machine learning method known as Generative Adversarial Networks (GANs). Then, we present a new deep learning structure to model both cyber-attacker and system defender and aslo flexibility of the system defender to learn different possible attacks. We also use another machine learning approach to mitigate the cyber-attacks effects. Particularly, we use Reinforcement Learning (RL) to investigate the optimal possible actions after the cyber-attack happens in the system. In order to model the possible attack's locations we use multi-stage game between the cyber-attacker and system defender. To model the attacker's moves, we use the Hamiltonian Markov Chain Monte Carlo (H-MCMC) and sample from the posterior distribution of the attack's locations. we then train a deep RL network to learn the optimal actions regarding given game stage and possible future game stages.



Cyber-security, Machine Learning, Deep Learning, Reinforcement Learning, Generative Adversarial Network, Mathematical Programming with Equality Constraint, Bayesian Inference, Hamiltonian Markov Chain


Portions of this document appear in: Ahmadian, Saeed, Heidar Malki, and Zhu Han. "Cyber attacks on smart energy grids using generative adverserial networks." In 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 942-946. IEEE, 2018. And in: Ahmadian, Saeed, Behrooz Vahidi, Jahandar Jahanipour, Seyed Hossein Hoseinian, and Hasan Rastegar. "Price restricted optimal bidding model using derated sensitivity factors by considering risk concept." IET Generation, Transmission & Distribution 10, no. 2 (2016): 310-324. And in: Ahmadian, Saeed, Xiao Tang, Heidar A. Malki, and Zhu Han. "Modelling cyber attacks on electricity market using mathematical programming with equilibrium constraints." IEEE Access 7 (2019): 27376-27388. And in: Ahmadian, Saeed, Saba Ebrahimi, and Ehsan Ghotbi. "Wind farm layout optimization problem using joint probability distribution of CVaR analysis." In 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0007-0012. IEEE, 2019.