Filters

20242
Reset filters

Settings

Author: Catak, Ferhat OzgurPublisher: Ieee-inst Electrical Electronics Engineers inc

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Article
    Citation - WoS: 7
    Citation - Scopus: 8
    Flexible and Lightweight Mitigation Framework for Distributed Denial-Of 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.
  • Thumbnail Image
    Article
    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.