Student:	Seyit Ahmet Nafiz BURNAZ
Supervisor:	Doç. Dr. Mustafa Şinasi AYAS
Department:	Elektrik-Elektronik Müh.
Institution:	Graduate School of Natural and Applied Sciences
University:	Karadeniz Technical University Turkey

Title of the Thesis:	DEEP LEARNING BASED DETECTION OF ATTACKS AGAINST STATE ESTIMATION IN SMART GRIDS
Level:	M.Sc.
Acceptance Date:	16/12/2022
Number of Pages:	50
Registration Number:	i4087

Summary:

Electricity grids, one of the critical infrastructures, have become vulnerable to

malicious attackers with the development of communication technologies. For this reason,

power systems are among the issues that need to be examined in terms of cyber security. The

safety of the state estimator using the measurements obtained from the electrical network is

crucial. Due to the nature of the sensors in a power system, errors and communication

problems may occur. The state estimator may fill in erroneous measurements due to sensor

and communication errors by correcting minor errors in the measurements, but an attacker

who has leaked the systems communication network could inject false data into the

measurements and bypasses traditional detection algorithms and causes errors in the power

system state variables. In this thesis, a deep learning-based detection algorithm is examined

for the detection of the attacks that traditional residue-based approaches cannot detect. In

addition, the False Data Injection (FDI) attack, which was previously proposed in the

literature, is mathematically modeled in this study. FDI attack was simulated using open

source MATGRID library in MATLAB by using IEEE 9 Bus test system. As a result of the

study, the attack vector obtained mathematically and state variables were corrupted and the

deep learning-based model performed well in detecting attacked measures.