Automated Forensic Examination of Virtual Assets Using XGBoost

Devaseelan S and B. M. Praveen

International Journal of Scientific Research in Science and Technology, 2024, vol. 11, issue 6, 220-227

Abstract: The issues of identification of virtual assets and digital currencies have become more acute in recent years because of increased numbers of transactions, their increased volumes, and complexity in forensic examination. This paper is devoted to a new method of the automated forensic investigation of virtual assets with the help of a powerful machine learning algorithm called XGBoost. XGBoost has very high speed and it is quite useful when dealing with large amounts of data and used for finding patterns that may suggest unlawful operations. The given framework is based on XGBoost by identifying the transactions using their features activities, amount, frequencies, volumes and addresses. Through training the model that is given the historical data, the model is, therefore, able to separate normal and suspicious transactions and alert the decision-making process for further review. Network analysis integration takes the system capability to a new level by enabling the identification of intricate transaction characteristics and the interconnectedness of addressing. Aside from adding automation to the forensic examinations to make it more efficient and accurate, such a procedure assists in identifying the developments of virtual asset transactions. The results show the effectiveness of the XGBoost in supporting the advance of forensic investigators helping them to meet the growing roles that cybercriminals portray in today’s digital asset markets.

Keywords: Automated Forensic related Examination; Virtual based Assets; XGBoost; Machine Learning; Transaction Analysis Process; Anomaly Detections; Digital Currency; Cybercrime Detection; Illicit Activities (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v11:y2024:i6:id:407

DOI: 10.32628/IJSRST24114976

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