Deep learning in intrusion detection perspective: Overview and further challenges

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

32 Citations (Scopus)

Abstract

Deep learning techniques are famous due to Its capability to cope with large-scale data these days. They have been investigated within various of applications e.g., language, graphical modeling, speech, audio, image recognition, video, natural language and signal processing areas. In addition, extensive researches applying machine-learning methods in Intrusion Detection System (IDS) have been done in both academia and industry. However, huge data and difficulties to obtain data instances are hot challenges to machine-learning-based IDS. We show some limitations of previous IDSs which uses classic machine learners and introduce feature learning including feature construction, extraction and selection to overcome the challenges. We discuss some distinguished deep learning techniques and its application for IDS purposes. Future research directions using deep learning techniques for IDS purposes are briefly summarized.

Original languageEnglish
Title of host publicationProceedings - WBIS 2017
Subtitle of host publication2017 International Workshop on Big Data and Information Security
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5-10
Number of pages6
ISBN (Electronic)9781538620380
DOIs
Publication statusPublished - 29 Jan 2018
Event2017 International Workshop on Big Data and Information Security, WBIS 2017 - Jakarta, Indonesia
Duration: 23 Sep 201724 Sep 2017

Publication series

NameProceedings - WBIS 2017: 2017 International Workshop on Big Data and Information Security
Volume2018-January

Conference

Conference2017 International Workshop on Big Data and Information Security, WBIS 2017
Country/TerritoryIndonesia
CityJakarta
Period23/09/1724/09/17

Keywords

  • Artificial Neural Network
  • Decision Tree
  • Feature Selection
  • Intrusion Detection System
  • Wi-Fi Network

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