Class imbalance aware drift identification model for detecting diverse attack in streaming environment

Arati Shahapurkar (K. L. S. Gogte Institute of Technology, Affiliated to Visveswarya Technological University)
Rudragoud Patil (K. L. S. Gogte Institute of Technology, Affiliated to Visveswarya Technological University)
Kiran K. Tangod (K. L. S. Gogte Institute of Technology, Affiliated to Visveswarya Technological University)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 33, No 2: February 2024

Detecting fraudulent transactions in a streaming environment presents several challenges including the large volume of data, the need for real-time detection, and the potential for data drift. To address these challenges a robust model is needed that utilizes machine learning techniques to classify transactions in real-time. Hence, this paper proposes a model for detecting fraudulent transactions in a streaming environment using xtream gradient boost (XGBoost), cross-validation and class imbalance aware drift identification (CIADI) model. The performance of the proposed method is evaluated using datasets named credit card and Network Security Laboratory (NSL-KDD) dataset. The results demonstrate that the model can effectively detect fraudulent transactions with high accuracy, recall, and F-measure. The results show that the proposed CIADI model attained 95.63% for the credit card dataset which is higher accuracy in comparison to the generative-adversarial networks (GAN), network-anomaly-detection scheme-based on feature-representation and data-augmentation (NADS-RA) and feature-aware XGBoost (FA-XGB). Further the proposed CIADI model attained 98.5% for the NSL-KDD dataset which is higher accuracy in comparison to the NADS-RA, stacked-nonsymmetric deep-autoencoder (sNDAE) and convolutional neural-network (CNN). This study suggests that the proposed method can be an effective model for detecting fraudulent transactions in streaming environments.