Deep Learning Models for Intrusion Detection in Wi-Fi Networks: A Literature Survey

Achmad Eriza Aminanto, Muhamad Erza Aminanto

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Recently, the number of devices that are connected to the Internet are increasing exponentially due to the rise of the Internet of Things (IoT) era. Despite many advancements of the IoT era, we have been exposed to cyber security threats. Moreover, in this Covid-19 pandemic situation, the trend of cyber crimes is also increasing sharply. In this paper, we discuss one of possible countermeasures to combat cyber threats, namely Intrusion Detection Systems (IDS). IDS usually leverage many different types of machine learning models to detect the unknown attacks. In order to avoid confusion for future researchers in this field, we examine several states of the art papers which leverage deep learning for IDS in Wi-Fi networks. For this purpose, we choose one common Wi-Fi networks dataset, called AWID dataset. By examining the recent studies, we are able to understand current problems of IDS in Wi-Fi networks and able to prepare the best machine learning model for the corresponding problem to achieve a safe environment with minimal risk of cyber threats.

Original languageEnglish
Title of host publicationSustainable Architecture and Building Environment - Proceedings of ICSDEMS 2020
EditorsLin Yola, Utaberta Nangkula, Olutobi Gbenga Ayegbusi, Mokhtar Awang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages115-121
Number of pages7
ISBN (Print)9789811623288
DOIs
Publication statusPublished - 2022
EventInternational Conference on Sustainable Design, Engineering, Management, and Sciences, ICSDEMS 2020 - Virtual, Online
Duration: 8 Dec 20209 Dec 2020

Publication series

NameLecture Notes in Civil Engineering
Volume161
ISSN (Print)2366-2557
ISSN (Electronic)2366-2565

Conference

ConferenceInternational Conference on Sustainable Design, Engineering, Management, and Sciences, ICSDEMS 2020
CityVirtual, Online
Period8/12/209/12/20

Keywords

  • Anomaly detection
  • AWID dataset
  • Deep learning
  • Intrusion detection system

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