Browsing by Author "Bozdal, Mehmet"

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Comparative analysis of dimensionality reduction techniques for cybersecurity in the SWaT dataset
(SPRINGER, 2024) Bozdal, Mehmet; Ileri, Kadir; Ozkahraman, Ali; 0000-0002-2081-7101; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Bozdal, Mehmet
The Internet of Things (IoT) has revolutionized the functionality and efciency of distributed cyber-physical systems, such as city-wide water treatment systems. However, the increased connectivity also exposes these systems to cybersecurity threats. This research presents a novel approach for securing the Secure Water Treatment (SWaT) dataset using a 1D Convolutional Neural Network (CNN) model enhanced with a Gated Recurrent Unit (GRU). The proposed method outperforms existing methods by achieving 99.68% accuracy and an F1 score of 98.69%. Additionally, the paper explores dimensionality reduction methods, including Autoencoders, Generalized Eigenvalue Decomposition (GED), and Principal Component Analysis (PCA). The research fndings highlight the importance of balancing dimensionality reduction with the need for accurate intrusion detection. It is found that PCA provided better performance compared to the other techniques, as reducing the input dimension by 90.2% resulted in only a 2.8% and 2.6% decrease in the accuracy and F1 score, respectively. This study contributes to the feld by addressing the critical need for robust cybersecurity measures in IoT-enabled water treatment systems, while also considering the practical trade-of between dimensionality reduction and intrusion detection accuracy.
Security through Digital Twin-Based Intrusion Detection: A SWaT Dataset Analysis
(Institute of Electrical and Electronics Engineers Inc., 2023) Bozdal, Mehmet; 0000-0002-2081-7101; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Bozdal, Mehmet
Digital twin, as a virtual replica of physical entity, offer valuable insights into Industrial Control System (ICS) behavior and characteristics. Leveraging the convergence of digital twins and cybersecurity, this research explores its role in securing critical infrastructure, using the Secure Water Treatment (SWaT) system as a case study. Existing intrusion detection systems (IDS) for SWaT encounter challenges related to requiring huge amounts of a dataset for training, being unable to adopt high data dimensionality, and adaptability to emerging threats. To address these issues, a hybrid digital twin model is proposed, combining physics-based models and data-driven approaches. This model facilitates precise attack localization and explainable IDS outcomes. The method exhibits promising capabilities for enhancing critical infrastructure security and adapting to evolving cyber threats. Experimental results demonstrate the ability to detect eight out of nine attack types.