Annotated Dataset for Anomaly Detection in a SDN infraestructure

4 pages•Published: February 16, 2023

Anxo Otero Dans and Víctor Manuel Carneiro Díaz

Abstract

Software-Defined Network (SDN) is an emerging architecture which objective is to re- duce the limitations of traditional IP networks by decoupling the network tasks performed on each device in certain planes by controlling and managing the whole network from a centralized location. However, this centralization also introduces new inefficiencies and vulnerabilities, such as those related to southbound and northbound controller interfaces, which often negatively affect security. In the past years, Machine Learning (ML) tech- niques have been implemented in SDN architectures to protect networks and solve security problems but sometimes it is difficult to obtain the right characteristics in real time. In this paper, we introduce a flow-based anomaly detection system in which the controller itself is in charge of receiving, analyzing and classifying the traffic by extracting a group of flow features.

Keyphrases: anomaly detection, dataset generation, sdn

In: Alvaro Leitao and Lucía Ramos (editors). Proceedings of V XoveTIC Conference. XoveTIC 2022, vol 14, pages 96-99.

Links:	https://easychair.org/publications/paper/R4HP
	https://doi.org/10.29007/gklh

BibTeX entry

@inproceedings{XoveTIC2022:Annotated_Dataset_Anomaly_Detection,
  author    = {Anxo Otero Dans and Víctor Manuel Carneiro Díaz},
  title     = {Annotated Dataset for Anomaly Detection in a SDN infraestructure},
  booktitle = {Proceedings of V XoveTIC Conference. XoveTIC 2022},
  editor    = {Alvaro Leitao and Lucía Ramos},
  series    = {Kalpa Publications in Computing},
  volume    = {14},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1762},
  url       = {/publications/paper/R4HP},
  doi       = {10.29007/gklh},
  pages     = {96-99},
  year      = {2023}}

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