Bozdal, Mehmet

Profile Picture
Profile URL
https://hdl.handle.net/20.500.12573/2773
Name Variants
Bozdal, Mehmet
Mehmet Bozdal
Job Title
Dr. Öğr. Üyesi
Email Address
mehmet.bozdal@agu.edu.tr
Main Affiliation
02.05. Elektrik & Elektronik Mühendisliği
Status
Current Staff
Website
ORCID ID
0000-0002-2081-7101
Scopus Author ID
57208000471
Turkish CoHE Profile ID
36213
Google Scholar ID
AR2tsh0AAAAJ
WoS Researcher ID
AAS-7971-2020

Sustainable Development Goals

5

GENDER EQUALITY
GENDER EQUALITY Logo

0

Research Products

11

SUSTAINABLE CITIES AND COMMUNITIES
SUSTAINABLE CITIES AND COMMUNITIES Logo

1

Research Products

3

GOOD HEALTH AND WELL-BEING
GOOD HEALTH AND WELL-BEING Logo

0

Research Products

4

QUALITY EDUCATION
QUALITY EDUCATION Logo

0

Research Products

17

PARTNERSHIPS FOR THE GOALS
PARTNERSHIPS FOR THE GOALS Logo

0

Research Products

9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
INDUSTRY, INNOVATION AND INFRASTRUCTURE Logo

2

Research Products

6

CLEAN WATER AND SANITATION
CLEAN WATER AND SANITATION Logo

0

Research Products

13

CLIMATE ACTION
CLIMATE ACTION Logo

0

Research Products

10

REDUCED INEQUALITIES
REDUCED INEQUALITIES Logo

0

Research Products

16

PEACE, JUSTICE AND STRONG INSTITUTIONS
PEACE, JUSTICE AND STRONG INSTITUTIONS Logo

0

Research Products

12

RESPONSIBLE CONSUMPTION AND PRODUCTION
RESPONSIBLE CONSUMPTION AND PRODUCTION Logo

0

Research Products

14

LIFE BELOW WATER
LIFE BELOW WATER Logo

0

Research Products

1

NO POVERTY
NO POVERTY Logo

0

Research Products

15

LIFE ON LAND
LIFE ON LAND Logo

0

Research Products

2

ZERO HUNGER
ZERO HUNGER Logo

0

Research Products

8

DECENT WORK AND ECONOMIC GROWTH
DECENT WORK AND ECONOMIC GROWTH Logo

0

Research Products

7

AFFORDABLE AND CLEAN ENERGY
AFFORDABLE AND CLEAN ENERGY Logo

1

Research Products
Scopus data could not be loaded because of an error. Please refresh the page or try again later.
WoS data could not be loaded because of an error. Please refresh the page or try again later.
Scholarly Output

6

Articles

1

Views / Downloads

2/0

Supervised MSc Theses

0

Supervised PhD Theses

0

WoS Citation Count

13

Scopus Citation Count

17

WoS h-index

1

Scopus h-index

2

Patents

0

Projects

0

WoS Citations per Publication

2.17

Scopus Citations per Publication

2.83

Open Access Source

0

Supervised Theses

0

JournalCount
-- 16th International Conference on Information Security and Cryptology, ISCTURKEY 2023 -- Ankara -- 1953121
16th Turkey International Conference on Information Security and Cryptology-ISCTURKEY-Annual -- Oct 18-19, 2023 -- Ankara, Turkiye1
17th International Conference on Innovative Security Solutions for Information Technology and Communications-SecITC -- Nov 21-22, 2024 -- Bucharest, Romania1
-- 2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024 -- Ankara -- 2045621
Journal of Supercomputing1
Current Page: 1 / 2

Scopus Quartile Distribution

Competency Cloud

GCRIS Competency Cloud

Filters

2023 - 20256
Reset filters

Settings

Scholarly Output Search Results

Now showing 1 - 6 of 6
  • Thumbnail Image
    Conference Object
    Cyber Threats to Green Hydrogen Production Within a Solar Microgrid
    (Springer International Publishing AG, 2025) Bozdal, Mehmet; Pourmirza, Zoya
    The transition towards sustainable energy systems depends heavily on the reliable operation of renewable energy infrastructure, which is increasingly interconnected and digitized. Therefore, ensuring cybersecurity resilience is essential for maintaining the reliability and safety of renewable energy systems in a rapidly evolving digital landscape. This paper investigates the economic implications of data integrity and system configuration attacks on a green hydrogen production system within a solar microgrid. Through a comprehensive analysis, the vulnerability of the system to cyber intrusions that manipulate relay settings, electricity prices, and hydrogen level, is examined. Drawing on a multidisciplinary framework encompassing energy economics, cybersecurity, and renewable energy technologies, a methodological approach is developed to quantify the direct economic impacts of attacks. Simulation results indicate that such attacks can decrease profits by up to 14%.
  • Thumbnail Image
    Conference Object
    Enhancing Intrusion Detection in Electric Networks Using Physics-Informed Random Forest
    (Institute of Electrical and Electronics Engineers Inc., 2024) Bozdal, Mehmet; Savasci, Alper
    The increasing complexity of electric power networks has heightened their vulnerability to cyber-attacks, challenging traditional Intrusion Detection Systems (IDS) that rely on manually crafted rules. This paper introduces a novel approach that integrates physics-informed features and feature selection into a Random Forest (RF) model to enhance IDS performance. By deriving features such as complex power and impedance from fundamental electrical principles and applying SelectKBest for optimal feature selection, our method not only improves detection accuracy but also enhances efficiency by using fewer than half the features. Specifically, the feature-enriched RF model utilizing 55 features achieves an accuracy of 0.9667 and an F1-score of 0.9664, compared to 0.9576 and 0.9570 for the baseline RF model. This approach demonstrates the effectiveness of advanced feature engineering and selection techniques for improving the security and reliability of power network monitoring systems. © 2024 Elsevier B.V., All rights reserved.
  • Thumbnail Image
    Conference Object
    Security Through Digital Twin-Based Intrusion Detection: A Swat Dataset Analysis
    (IEEE, 2023) 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.
  • Thumbnail Image
    Conference Object
    Temporal Logic-Based Intrusion Detection for Securing Connected Vehicles
    (Springer International Publishing AG, 2024) Bozdal, Mehmet
    Ensuring the security and integrity of in-vehicle communication networks (IVCNs) is paramount. The increasing connectivity of vehicles exposes them to unprecedented security vulnerabilities, necessitating innovative methodologies to safeguard against cyberattacks and unauthorized access. This research presents a novel approach to enhance IVCN security through the deployment of a Signal Temporal Logic (STL)-based Intrusion Detection System (IDS). Considering the limited resources of Electronic Control Units (ECUs), this approach offers an adaptive and lightweight solution that addresses the unique challenges posed by the dynamic nature of vehicular networks. The proposed STL-based IDS effectively detects a broad spectrum of intrusions while maintaining acceptable overhead for resource-constrained ECUs, thanks to its distributed architecture. Comprehensive experimental evaluations demonstrate significant performance improvements in detecting Denial of Service (DoS) attacks, achieving the highest accuracy of 0.996 and recall of 1.000. The system also excels in detecting fuzzy attacks, with the highest accuracy of 0.996.
  • Thumbnail Image
    Article
    Citation - WoS: 13
    Citation - Scopus: 14
    Comparative Analysis of Dimensionality Reduction Techniques for Cybersecurity in the SwaT Dataset
    (Springer, 2024) Bozdal, Mehmet; Ileri, Kadir; Ozkahraman, Ali
    The Internet of Things (IoT) has revolutionized the functionality and efficiency 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 findings 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 field by addressing the critical need for robust cybersecurity measures in IoT-enabled water treatment systems, while also considering the practical trade-off between dimensionality reduction and intrusion detection accuracy.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 3
    Security Through Digital Twin-Based Intrusion Detection: A SwaT Dataset Analysis
    (Institute of Electrical and Electronics Engineers Inc., 2023) 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. © 2024 Elsevier B.V., All rights reserved.