Deep Learning-Based Detection of Cyberattacks in Software-Defined Networks

Seyed Mohammad Hadi Mirsadeghi, Hayretdin Bahsi, Wissem Inbouli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

This paper presents deep learning models for binary and multiclass intrusion classification problems in Software-defined-networks (SDN). The induced models are evaluated by the state-of-the-art dataset, InSDN. We applied Convolutional Autoencoder (CNN-AE) for high-level feature extraction, and Multi-Layer Perceptron (MLP) for classification that delivers high-performance metrics of F1-score, accuracy and recall compared to similar studies. Highly imbalanced datasets such as InSDN underperform in detecting the instances belonging to the minority class. We use Synthetic Minority Oversampling Technique (SMOTE) to address dataset imbalance and observe a significant detection enhancement in the detection of minority classes.

Original languageEnglish (US)
Title of host publicationDigital Forensics and Cyber Crime - 13th EAI International Conference, ICDF2C 2022, Proceedings
EditorsSanjay Goel, Akatyev Nikolay, Daryl Johnson, Pavel Gladyshev, George Markowsky
PublisherSpringer Science and Business Media Deutschland GmbH
Pages341-354
Number of pages14
ISBN (Print)9783031365737
DOIs
StatePublished - 2023
Externally publishedYes
Event13th EAI International Conference on Digital Forensics and Cyber Crime, ICDF2C 2022 - Boston, United States
Duration: Nov 16 2022Nov 18 2022

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume508 LNICST
ISSN (Print)1867-8211
ISSN (Electronic)1867-822X

Conference

Conference13th EAI International Conference on Digital Forensics and Cyber Crime, ICDF2C 2022
Country/TerritoryUnited States
CityBoston
Period11/16/2211/18/22

Keywords

  • Dataset Balancing
  • Deep Learning
  • Intrusion Detection
  • Software-Defined Network

ASJC Scopus subject areas

  • Computer Networks and Communications

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