TY - JOUR
T1 - Bio-inspired algorithms for cybersecurity - a review of the state-of-the-art and challenges
AU - Chui, Kwok Tai
AU - Liu, Ryan Wen
AU - Zhao, Mingbo
AU - Zhang, Xinyu
N1 - Publisher Copyright:
© 2024 Inderscience Publishers. All rights reserved.
PY - 2024/1/22
Y1 - 2024/1/22
N2 - 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.
AB - 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.
KW - bio-inspired algorithms
KW - cybersecurity
KW - ecology-based algorithm
KW - evolutionary algorithms
KW - machine learning
KW - multi-objective optimisation
KW - swarm intelligence
KW - trade-off solution
UR - http://www.scopus.com/inward/record.url?scp=85183585133&partnerID=8YFLogxK
U2 - 10.1504/IJBIC.2024.136199
DO - 10.1504/IJBIC.2024.136199
M3 - Article
AN - SCOPUS:85183585133
SN - 1758-0366
VL - 23
SP - 1
EP - 15
JO - International Journal of Bio-Inspired Computation
JF - International Journal of Bio-Inspired Computation
IS - 1
ER -