Version 1
: Received: 19 August 2023 / Approved: 21 August 2023 / Online: 22 August 2023 (16:36:05 CEST)
How to cite:
Tavangari, S. A Brief Research in Machine Learning-Driven Classification of DDoS Attacks in SDN Environment. Preprints2023, 2023081589. https://doi.org/10.20944/preprints202308.1589.v1
Tavangari, S. A Brief Research in Machine Learning-Driven Classification of DDoS Attacks in SDN Environment. Preprints 2023, 2023081589. https://doi.org/10.20944/preprints202308.1589.v1
Tavangari, S. A Brief Research in Machine Learning-Driven Classification of DDoS Attacks in SDN Environment. Preprints2023, 2023081589. https://doi.org/10.20944/preprints202308.1589.v1
APA Style
Tavangari, S. (2023). A Brief Research in Machine Learning-Driven Classification of DDoS Attacks in SDN Environment. Preprints. https://doi.org/10.20944/preprints202308.1589.v1
Chicago/Turabian Style
Tavangari, S. 2023 "A Brief Research in Machine Learning-Driven Classification of DDoS Attacks in SDN Environment" Preprints. https://doi.org/10.20944/preprints202308.1589.v1
Abstract
In the landscape of network management, software-defined networking (SDN) technology emerges as a dynamic approach, facilitating efficient network configuration for improved performance and monitoring, akin to the agility of cloud computing rather than traditional methods. However, its centralized structure exposes SDN to various attack vectors. Distributed Denial of Service (DDoS) attacks, particularly, pose a significant threat to SDN. This study employs machine learning algorithms and Network Traffic Classification Analysis (NCA) to classify SDN traffic as potential attacks. Notably, Decision Trees (DT) out shine other algorithms with a flawless 100% classification success, spotlighting their supremacy. Through this research, a robust path toward fortified SDN security takes form, where technological prowess and strategic intelligence unite for enhanced defense.
Keywords
SDN; Machine Learning; Algorithms; Network
Subject
Computer Science and Mathematics, Computer Networks and Communications
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
