Yazar "Catak, Ferhat Ozgur" seçeneğine göre listele

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    Federated learning: Overview, strategies, applications, tools and future directions
    (Elsevier Ltd, 2024) Yurdem, Betul; Kuzlu, Murat; Gullu, Mehmet Kemal; Catak, Ferhat Ozgur; Tabassum, Maliha
    Federated learning (FL) is a distributed machine learning process, which allows multiple nodes to work together to train a shared model without exchanging raw data. It offers several key advantages, such as data privacy, security, efficiency, and scalability, by keeping data local and only exchanging model updates through the communication network. This review paper provides a comprehensive overview of federated learning, including its principles, strategies, applications, and tools along with opportunities, challenges, and future research directions. The findings of this paper emphasize that federated learning strategies can significantly help overcome privacy and confidentiality concerns, particularly for high-risk applications. © 2024 The Author(s)
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    Flexible and Lightweight Mitigation Framework for Distributed Denial-of-Service Attacks in Container-Based Edge Networks Using Kubernetes
    (Ieee-Inst Electrical Electronics Engineers Inc, 2024) Koksal, Sarp; Catak, Ferhat Ozgur; Dalveren, Yaser
    Mobile Edge Computing (MEC) has a significant potential to become more prevalent in Fifth Generation (5G) networks, requiring resource management that is lightweight, agile, and dynamic. Container-based virtualization platforms, such as Kubernetes, have emerged as key enablers for MEC environments. However, network security and data privacy remain significant concerns, particularly due to Distributed Denial-of-Service (DDoS) attacks that threaten the massive connectivity of end-devices. This study proposes a defense mechanism to mitigate DDoS attacks in container-based MEC networks using Kubernetes. The mechanism dynamically scales Containerized Network Functions (CNFs) with auto-scaling through an Intrusion Detection and Prevention System (IDPS). The architecture of the proposed mechanism leverages distributed edge clusters and Kubernetes to manage resources and balance the load of IDPS CNFs. Experiments conducted in a real MEC environment using OpenShift and Telco-grade MEC profiles demonstrate the effectiveness of the proposed mechanism against Domain Name System (DNS) flood and Yo-Yo attacks. Results also verify that Kubernetes efficiently meets the lightweight, agile, and dynamic resource management requirements of MEC networks.
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    Radar Emitter Localization Based on Multipath Exploitation Using Machine Learning
    (Ieee-Inst Electrical Electronics Engineers Inc, 2024) Catak, Ferhat Ozgur; Al Imran, Md Abdullah; Dalveren, Yaser; Yildiz, Beytullah; Kara, Ali
    In this study, a Machine Learning (ML)-based approach is proposed to enhance the computational efficiency of a particular method that was previously proposed by the authors for passive localization of radar emitters based on multipath exploitation with a single receiver in Electronic Support Measures (ESM) systems. The idea is to utilize a ML model on a dataset consisting of useful features obtained from the priori-known operational environment. To verify the applicability and computational efficiency of the proposed approach, simulations are performed on the pseudo-realistic scenes to create the datasets. Well-known regression ML models are trained and tested on the created datasets. The performance of the proposed approach is then evaluated in terms of localization accuracy and computational speed. Based on the results, it is verified that the proposed approach is computationally efficient and implementable in radar detection applications on the condition that the operational environment is known prior to implementation.
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    Serverless federated learning: Decentralized spectrum sensing in heterogeneous networks
    (Elsevier, 2025) Catak, Ferhat Ozgur; Kuzlu, Murat; Dalveren, Yaser; Ozdemir, Gokcen
    Federated learning (FL) has gained more popularity due to the increasing demand for robust and efficient mechanisms to ensure data privacy and security during collaborative model training in the concept of artificial intelligence/machine learning (AI/ML). This study proposes an advanced version of FL without the central server, called a serverless or decentralized federated learning framework, to address the challenge of cooperative spectrum sensing in non-independent and identically distributed (non-IID) environments. The framework leverages local model aggregation at neighboring nodes to improve robustness, privacy, and generalizability. The system incorporates weighted aggregation based on distributional similarity between local datasets using Wasserstein distance. The results demonstrate that the proposed serverless federated learning framework offers a satisfactory performance in terms of accuracy and resilience.