A Deep Long Short-Term Memory based classifier for Wireless Intrusion Detection System

Sydney Mambwe Kasongo, Yanxia Sun

Research output: Contribution to journalArticlepeer-review

45 Citations (Scopus)

Abstract

Wireless networks have evolved over the years and they have become some of the most prominent communication media. These networks generally transmit large volumes of information at any given time. This has engendered a number of security threats and privacy concerns. This paper presents a Deep Long Short-Term Memory (DLSTM) based classifier for wireless intrusion detection system (IDS). Using the NSL-KDD dataset, we compare the DLSTM IDS to existing methods such as Deep Feed Forward Neural Networks, Support Vector Machines, k-Nearest Neighbors, Random Forests and Naïve Bayes. The experimental results suggest that the DLSTM IDS outperformed existing approaches.

Original languageEnglish
Pages (from-to)98-103
Number of pages6
JournalICT Express
Volume6
Issue number2
DOIs
Publication statusPublished - Jun 2020

Keywords

  • Deep learning
  • Intrusion detection
  • Machine learning
  • Wireless networks

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence

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