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

Abstract

<div align="center"><span>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.</span></div>

Text Mining Algorithm Naive Bayes Classifier to Improve Quality Sentiment Analysis Passport Mobile Application

“…The selected seed will be used as a parameter of the random function. Furthermore, classification is carried out by implementing the Naive Bayes classifier algorithm for positive sentiment and negative sentiment, in the process of text mining, of course using the confusion matrix method in the evaluation process to find out the accuracy value of the algorithm used (Shahpurkar et al, 2024). Confusion matrix is one of the important tools in evaluation methods used in machine learning which usually contains two or more categories.…”