Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2024Scopus SJRQ1/r/munzur/2283
  Ensemble Learning Framework for DDoS Detection in SDN-Based SCADA Systems

  Oyucu, Saadin; Polat, Onur; Türkoğlu, Muammer; Polat, Hüseyin; Aksöz, Ahmet; Ağdaş, Mehmet Tevfik

  Supervisory Control and Data Acquisition (SCADA) systems play a crucial role in overseeing and controlling renewable energy sources like solar, wind, hydro, and geothermal resources. Nevertheless, with the expansion of conventional SCADA network infrastructures, there arise significant challenges in managing and scaling due to increased size, complexity, and device diversity. Using Software Defined Networking (SDN) technology in traditional SCADA network infrastructure offers management, scaling and flexibility benefits. However, as the integration of SDN-based SCADA systems with modern technologies such as the Internet of Things, cloud computing, and big data analytics increases, cybersecurity becomes a major concern for these systems. Therefore, cyber-physical energy systems (CPES) should be considered together with all energy systems. One of the most dangerous types of cyber-attacks against SDN-based SCADA systems is Distributed Denial of Service (DDoS) attacks. DDoS attacks disrupt the management of energy resources, causing service interruptions and increasing operational costs. Therefore, the first step to protect against DDoS attacks in SDN-based SCADA systems is to develop an effective intrusion detection system. This paper proposes a Decision Tree-based Ensemble Learning technique to detect DDoS attacks in SDN-based SCADA systems by accurately distinguishing between normal and DDoS attack traffic. For training and testing the ensemble learning models, normal and DDoS attack traffic data are obtained over a specific simulated experimental network topology. Techniques based on feature selection and hyperparameter tuning are used to optimize the performance of the decision tree ensemble models. Experimental results show that feature selection, combination of different decision tree ensemble models, and hyperparameter tuning can lead to a more accurate machine learning model with better performance detecting DDoS attacks against SDN-based SCADA systems.

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  AtıfScopus36·OpenAlex27

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• MakaleAçık Erişim2023/r/munzur/5745
  Deep Learning-Based Automatic Helmet Detection System in Construction Site Cameras

  Korkmaz, Adem; Ağdaş, Mehmet Tevfik

  Ensuring worker safety in high-risk environments such as construction sites is of paramount importance. Personal protective equipment, particularly helmets, plays a critical role in preventing severe head injuries. This study aims to develop an automated helmet detection system using the state-of-the-art YOLOv8 deep learning model to enhance safety monitoring in real-time. The dataset used for the study consists of 16,867 images, with various data augmentation and preprocessing techniques applied to improve the model's robustness. The YOLOv8 model achieved a 96.9% mAP50 score, outperforming other deep learning models in similar studies. The results demonstrate the effectiveness of the YOLOv8 model for accurate and efficient helmet detection in construction sites, paving the way for improved safety monitoring and enforcement in the construction industry.

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  AtıfOpenAlex15·TR Dizin3

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