Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
research-article

Anomaly-based intrusion detection system using Harris Hawks optimisation with a sigmoid neuron network

Published: 30 July 2024 Publication History

Abstract

This study introduces an innovative approach, merging Harris Hawks optimisation (HHO) with a sigmoid neuron network (SN), to enhance anomaly-based intrusion detection systems (ADS) performance. The resultant SN-HHO hybrid model aims to elevate detection rates and lower false positive rates (FPRs) within ADS. Evaluation across five datasets - UNSW-NB15, CIDDS-001, NSL-KDD, AWID3, and CICDDoS2019 - reveals heightened accuracy and faster convergence compared to existing methods. This work underscores the potential synergy of meta-heuristic optimisation and artificial neural networks, offering a promising strategy to fortify IDS performance and reliability, thus presenting a novel direction for advancing anomaly detection practices.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image International Journal of Information and Computer Security
International Journal of Information and Computer Security  Volume 24, Issue 1-2
2024
157 pages
EISSN:1744-1773
DOI:10.1504/ijics.2024.24.issue-1-2
Issue’s Table of Contents

Publisher

Inderscience Publishers

Geneva 15, Switzerland

Publication History

Published: 30 July 2024

Author Tags

  1. intrusion detection system
  2. IDS
  3. neural network
  4. meta-heuristic optimisation
  5. machine learning

Qualifiers

  • Research-article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 0
    Total Downloads
  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 06 Oct 2024

Other Metrics

Citations

View Options

View options

Get Access

Login options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media