In:
Bulletin of Electrical Engineering and Informatics, Institute of Advanced Engineering and Science, Vol. 12, No. 4 ( 2023-08-01), p. 2340-2347
Abstract:
An incursion into the computer network or system in issue occurs whenever there is an attempt made to circumvent the defences that are in place. Training and examination are the two basic components that make up the intrusion detection system (IDS) and each one may be analysed separately. During training, a number of distinct models are built, each of which is able to distinguish between normal and abnormal behaviours that are included within the dataset. This article proposes a combination of ant colony optimization (ACO) and the firefly approach for feature selection. The final outcome of giving careful thought to the selection of features will eventually result in greater accuracy of categorisation. When classifying various sorts of features, we make use of a wide variety of machine learning (ML) algorithms, including AdaBoost, gradient boost, and Bayesian network (BN), amongst others. The tests and assessments made use of data obtained from three distinct datasets, namely NSL-KDD, UNSW-NB15, and CICIDS 2017. The degree of performance of an individual may be broken down into its component parts, which include the F1 score, accuracy, precision, and recall. Gradient boost performs far better when it comes to recognising and classifying incursions.
Type of Medium:
Online Resource
ISSN:
2302-9285
,
2089-3191
DOI:
10.11591/beei.v12i4.4708
Language:
Unknown
Publisher:
Institute of Advanced Engineering and Science
Publication Date:
2023
Permalink