A deep learning method with filter based feature engineering for wireless intrusion detection system

Sydney Mambwe Kasongo, Yanxia Sun

Research output: Contribution to journalArticlepeer-review

152 Citations (Scopus)

Abstract

In recent years, the increased use of wireless networks for the transmission of large volumes of information has generated a myriad of security threats and privacy concerns; consequently, there has been the development of a number of preventive and protective measures including intrusion detection systems (IDS). Intrusion detection mechanisms play a pivotal role in securing computer and network systems; however, for various IDS, the performance remains a major issue. Moreover, the accuracy of existing methodologies for IDS using machine learning is heavily affected when the feature space grows. In this paper, we propose a IDS based on deep learning using feed forward deep neural networks (FFDNNs) coupled with a filter-based feature selection algorithm. The FFDNN-IDS is evaluated using the well-known NSL-knowledge discovery and data mining (NSL-KDD) dataset and it is compared to the following existing machine learning methods: Support vectors machines, decision tree, K-Nearest Neighbor, and Naïve Bayes. The experimental results prove that the FFDNN-IDS achieves an increase in accuracy in comparison to other methods.

Original languageEnglish
Article number8668403
Pages (from-to)38597-38607
Number of pages11
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 2019

Keywords

  • Deep learning
  • feature extraction
  • intrusion detection
  • machine learning
  • wireless networks

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering

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