Bio-inspired algorithms for cybersecurity - a review of the state-of-the-art and challenges

Kwok Tai Chui, Ryan Wen Liu, Mingbo Zhao, Xinyu Zhang

Research output: Contribution to journalArticlepeer-review

Abstract

It is witnessed that the popularity of the research in cybersecurity using bio-inspired algorithms (a key subset of natural algorithms) is ever-growing. As an emergent research area, researchers have devoted efforts to applying and comparing various bio-inspired algorithms to cybersecurity applications. It is necessary to have a systematic review of bio-inspired algorithms for cybersecurity to fill the gap in the missing research study on this topic. The research contributions of this review article are four-fold. It first highlights the foundation of the baseline and latest development of 12 popular bio-inspired algorithms in three categories namely ecology-based, evolutionary-based and swarm intelligence-based algorithms. A systematic review is conducted to synthesise and compare the research methodologies, results and limitations. In-depth discussion will be made on the shortlisted and highly cited articles. The tips to select appropriate algorithm or the combination of multiple algorithms have been reported, along with the pros and cons on the design and formulations. Future research directions will be presented to meet the trends and unexplored research.

Original languageEnglish
Pages (from-to)1-15
Number of pages15
JournalInternational Journal of Bio-Inspired Computation
Volume23
Issue number1
DOIs
Publication statusPublished - 22 Jan 2024

Keywords

  • bio-inspired algorithms
  • cybersecurity
  • ecology-based algorithm
  • evolutionary algorithms
  • machine learning
  • multi-objective optimisation
  • swarm intelligence
  • trade-off solution

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