Performance Evaluation of Homogenous Boosting Technique for Online Banking Network Intrusion Detection

Folahan Jiboku, Wumi Ajayi & Kayode Oladapo, Volume 4 Issue 2, December 2023 Pages 62-75, Published: 2023-12-18

Abstract

In recent times, it has been observed that a lot of users have been using online banking. However, security of online banking has been a matter of great concern for most users. This paper presents a performance evaluation of a homogeneous boosting technique for online banking network intrusion detection. The study aims to determine the effectiveness of the boosting technique in improving the detection of network intrusion attempts in online banking systems. The research methodology includes applying fuzzy logic feature selection technique on the dataset to determine the objectivity of the homogenous boosting ensemble machine learning algorithms. The experimental results of the study showed that the homogenous boosting technique performed well on the datasets, achieving high levels of accuracy and recall. The study also showed that the homogeneous boosting technique has a relatively low false-positive rate, indicating a high level of precision in detecting network intrusion attempts. Furthermore, the study evaluated the impact of various feature selection techniques on the performance of the boosting technique. The results demonstrate that the boosting technique performed better with selected feature subsets, which implies that the technique can be optimized for different online banking network intrusion detection scenarios. In conclusion, this paper demonstrated the effectiveness of the homogeneous boosting technique for online banking network intrusion detection. The study provides valuable insights into the use of boosting techniques and feature selection for improving the detection of network intrusion attempts in online banking systems. The findings of this study could help enhance the security of online banking systems and improve the overall trust of customers in online banking