Version 1
: Received: 26 February 2024 / Approved: 27 February 2024 / Online: 27 February 2024 (15:51:37 CET)
How to cite:
Ige, T.; Kiekintveld, C.; Piplai, A. An Investigation into the Performances of the State-of-the-art Machine Learning Approaches for Various Cyber-attack Detection: A Survey. Preprints2024, 2024021567. https://doi.org/10.20944/preprints202402.1567.v1
Ige, T.; Kiekintveld, C.; Piplai, A. An Investigation into the Performances of the State-of-the-art Machine Learning Approaches for Various Cyber-attack Detection: A Survey. Preprints 2024, 2024021567. https://doi.org/10.20944/preprints202402.1567.v1
Ige, T.; Kiekintveld, C.; Piplai, A. An Investigation into the Performances of the State-of-the-art Machine Learning Approaches for Various Cyber-attack Detection: A Survey. Preprints2024, 2024021567. https://doi.org/10.20944/preprints202402.1567.v1
APA Style
Ige, T., Kiekintveld, C., & Piplai, A. (2024). An Investigation into the Performances of the State-of-the-art Machine Learning Approaches for Various Cyber-attack Detection: A Survey. Preprints. https://doi.org/10.20944/preprints202402.1567.v1
Chicago/Turabian Style
Ige, T., Christophet Kiekintveld and Aritran Piplai. 2024 "An Investigation into the Performances of the State-of-the-art Machine Learning Approaches for Various Cyber-attack Detection: A Survey" Preprints. https://doi.org/10.20944/preprints202402.1567.v1
Abstract
To secure computers and information systems from attackers taking advantage of vulnerabilities in the system to commit cybercrime, several methods have been proposed for real-time detection of vulnerabilities to improve security around information systems. Of all the proposed methods, machine learning had been the most effective method in securing a system with capabilities ranging from early detection of software vulnerabilities to real-time detection of ongoing compromise in a system. As there are different types of cyberattacks, each of the existing state-of-the-art machine learning models depends on different algorithms for training which also impact their suitability for detection of a particular type of cyberattack. In this research, we analyzed each of the current state-of-theart machine learning models for different types of cyberattack detection from the past 10 years with a major emphasis on the most recent works for comparative study to identify the knowledge gap where work is still needed to be done with regard to detection of each category of cyberattack
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
