Abstract
Most of the intrusion detection systems are developed based on optimization algorithms as a resultof the increase in audit data features; optimization algorithms are also considered for IDS due to the decline in theperformance of the human-based methods in terms of their training time and classification accuracy. This articlepresents the development of an improved intrusion detection method for binary classification. In the proposed IDS,Rao Optimization Algorithm, Support Vector Machine (SVM), Extreme Learning Machine (ELM), and LogisticRegression (LR) (feature selection and weighting) were combined with NTLBO algorithm with supervised MLtechniques (for feature subset selection (FSS). Being that feature subset selection is considered a multi-objectiveoptimization problem, this study proposed the Rao-SVM as an FSS mechanism; its algorithm-specific and parameter-less concept was also explored. The prominent intrusion machine-learning dataset, UNSW-NB15, was used for theexperiments and the results showed that Rao-SVM reached 92.5% accuracy on the UNSW-NB15 dataset
Recommended Citation
Abd, Shamis N.; Alsajri, Mohammad; and Ibraheem, Hind Raad
(2024)
"Rao-SVM Machine Learning Algorithm for Intrusion Detection System,"
Iraqi Journal for Computer Science and Mathematics: Vol. 1:
Iss.
1, Article 5.
DOI: https://doi.org/10.52866/ijcsm.2019.01.01.004
Available at:
https://ijcsm.researchcommons.org/ijcsm/vol1/iss1/5
