ANALYSIS OF DDoS ATTACK DETECTION TECHNIQUES FOR SECURING SOFTWARE-DEFINED NETWORKS

Authors

  • Danijel Čabarkapa Academy of Professional Studies Šabac, Department of Medical and Business-Technological Studies
  • Brankica Popović
  • Petar Čisar
  • Kristijan Kuk

Keywords:

software-defined networking, virtualization, distributed denial of service attack, entropy, machine learning, detection schemes

Abstract

Software-Defined Networks (SDN) is an important technology that enables a new approach to how we develop and manage networks. SDN divides the data plane and control plane and supports the logical centralization of network control. However, the centralized architecture of SDN is also a potential vulnerability for various types of malicious attacks. The paper elaborates on the security aspects of virtualization as a basic concept of SDN architecture. Among the many types of attack, one of the most frequent and destructive are Distributed Denial of Service (DDoS) attacks. This paper presents an analysis of techniques to detect DDoS attacks in SDN networks. It first describes the SDN architecture and then elaborates on different detection techniques for DDoS attacks. Additionally, this paper emphasizes the types, components, and categories of detection solutions according to the techniques or methods used. The important approaches and those that can answer the complexity of detecting DDoS attacks in SDN are the detection schemes based on entropy and machine learning principles. This paper in general focuses on these two detection techniques and summarizes their benefits and drawbacks and finally provides a guideline for future research directions related to DDoS detection techniques in SDN networks.

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Published

2023-04-06

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Section

Natural and Applied Sciences in Forensics, Cybercrime and Security