Early Warning System for Accounting Fraud based on Internet Protocol(IP) Identification and Storage Algorithm

This research study explores the development of an early warning system for accounting fraud based on IP identification and storage algorithms. The proposed approach includes a novel method for IP identification and fraud detection that is both efficient and sensitive. For the IP address detection, the Tor anonymous access traffic identification is the key step. Identifying and classifying Tor anonymous traffic poses a significant challenge due to its scarcity in the Internet traffic, in the design, the XGBoost is used for optimization. To ensure accurate storage of detected information, a system log information storage model is also proposed. The method is tested using an accounting fraud application scenario, which demonstrates its effectiveness in detecting fraudulent activities. The proposed approach has significant potential to improve fraud detection and prevention in various industries. Future studies will investigate the various applications of this approach and its potential impact on different industries.