SHILL BIDDING FRAUD DETECTION IN ONLINE AUCTIONS USING MACHINE LEARNING ALGORITHMS

Abstract

As network traffic gets more complex, conventional manual techniques of identifying network traffic arebecoming less successful. Fraudulent activities are no longer allowed by internet auction sites like those thatallow shill bidding; hence, they are insufficient. In this project, we will: develop a hybrid machine-basedefficient Shill Bidding Fraud Detection (SBFD) system; recursive feature elimination (RFE) for featureselection enhances learning. The suggested model combines logistic regression, support vector machines(SVM), and In a voting ensemble structure, the Random Forest (RF) approach provides better categorization.performance and durability. The data for the network traffic of the system was preprocessed before evaluationand training. Categorical throughout the board Features were standardized; variables were label encoded.RFE is used to pick the top 10. Features were chosen from the original 41 in order to reduce computing costsand improve accuracy; the hybrid model was then trained on 80% of the data and evaluated on the remaining20% with exceptional precision of 97. 72%, a F1 score of 0. 98, and a ROC-AUC score of more than thereference study's stated 96% accuracy and 94% precision at 0. 999. paper. Among the assessment markersconfirmed were precision, recall, confusion matrix, and ROC-AUC curve. The model's great reliability inidentifying both shill attackers and normal activity. The outcome was that saved for more investigation werethe actual, predicted, and probability scores of the paper. Our ensemble method employs a hybrid model(SVM + Decision Tree) with an added logistic. The regression classifier showed better generalization anddetecting performance.