Yazar "Catak, Ferhat Ozgur" seçeneğine göre listele

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    Analysis of deceptive data attacks with adversarial machine learning for solar photovoltaic power generation forecasting
    (Springer, 2024) Kuzlu, Murat; Sarp, Salih; Catak, Ferhat Ozgur; Cali, Umit; Zhao, Yanxiao; Elma, Onur; Guler, Ozgur
    The solar photovoltaics (PV) energy resources have become more important with their significant contribution to the current power grid among renewable energy resources. However, the integration of the solar PV causes reliability issues in the power grid due to its high dependence on the weather condition. The predictability and stability of forecasting are critical for fully utilizing solar power. This study presents an Artificial Neural Network (ANN)-based solar PV power generation forecasting using a public dataset to form a basis experimental testbed to demonstrate analysis and impact of deceptive data attacks with adversarial machine learning. In addition, it evaluates the algorithms' performance using the Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Average Error (MAE) metrics for two main cases, i.e., with and without adversarial machine learning attacks. The results show that the ANN-based models are vulnerable to adversarial attacks.
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    Öğe
    Cybersecurity and Digital Privacy Aspects of V2X in the EV Charging Structure
    (Assoc Computing Machinery, 2023) Cali, Umit; Kuzlu, Murat; Elma, Onur; Gucluturk, Osman Gazi; Kilic, Ahmet; Catak, Ferhat Ozgur
    With the advancement of green energy technology and rising public and political acceptance, electric vehicles (EVs) have grown in popularity. Electric motors, batteries, and charging systems are considered major components of EVs. The electric power infrastructure has been designed to accommodate the needs of EVs, with an emphasis on bidirectional power flow to facilitate power exchange. Furthermore, the communication infrastructure has been enhanced to enable cars to communicate and exchange information with one another, also known as Vehicle-to-Everything (V2X) technology. V2X is positioned to become a bigger and smarter system in the future of transportation, thanks to upcoming digital technologies like Artificial Intelligence (AI), Distributed Ledger Technology, and the Internet of Things. However, like with any technology that includes data collection and sharing, there are issues with digital privacy and cybersecurity. This paper addresses these concerns by creating a multi-layer Cyber-Physical-Social Systems (CPSS) architecture to investigate possible privacy and cybersecurity risks associated with V2X. Using the CPSS paradigm, this research explores the interaction of EV infrastructure as a very critical part of the V2X ecosystem, digital privacy, and cybersecurity concerns.