Random Forest-Based Forensic Investigation of Non-Fungible Tokens: for Enhanced Detection and Anomaly Identification - Dr. Devaseelan S; Dr. B.M. Praveen (International Journal of Scientific Research in Science and Technology, 2024)
The proposed work builds upon the Random Forest machine learning algorithm to improve the process of digit forensic investigation in case of NFT. The following structure of this framework is aimed at identifying and disabling fraudulent or suspicious activities in NFT transactions by comparing different parameters like the Detection Time, False Positive Rate, the Total Transaction Volume Analyzed, the Anomalous Transaction Ratio, Clustering Accuracy, Data Utilization Efficiency, and Detection Sensitivity. Through using Random Forest, a solid ensemble learning technique that is well known for its on high accuracy as well as off overfitting tendency, it optimistically improves the identifying abilities of the framework in isolation of the false positives. The ability of the proposed system to deliver optimal results is further explained by line plots, area charts, histograms, and stem plots which all provide the variation of these metrics as the time proceeds. Not only does it enhance the effectiveness of detecting the fraudulent transactions, but it also enhances the application of data in the forensic analysis that creates a great advantage in the increasing realm of digital assets for investigators.
Keywords: Random Forest; Machine Learning; Forensic Investigation; Non-Fungible Tokens As NFTS; Anomaly Detection; Fraud Detection; Use of Data; Clustering Accuracy; Transaction Examination; Digital Assets