18181018. FRAUD DETECTION IN NFT EXCHANGES simplified abstract (Adobe Inc.)
Contents
FRAUD DETECTION IN NFT EXCHANGES
Organization Name
Inventor(s)
Harshvardhan . of Bokaro Steel City (IN)
Shambhavi Pardhi of Balaghat (IN)
Shaddy Garg of Baghapurana (IN)
Shiv Kumar Saini of Bangalore (IN)
Ramasuri Narayanam of Bangalore (IN)
FRAUD DETECTION IN NFT EXCHANGES - A simplified explanation of the abstract
This abstract first appeared for US patent application 18181018 titled 'FRAUD DETECTION IN NFT EXCHANGES
- Simplified Explanation:**
The patent application describes systems and methods for detecting fraudulent activity in NFT exchanges by analyzing transaction data and using a machine learning model to predict potential fraud.
- Key Features and Innovation:**
- Obtain transaction data for NFTs and create a transaction graph.
- Identify cycles in the transaction graph to predict fraudulent activity.
- Utilize a machine learning model to make predictions.
- Alert users about potential fraudulent activity.
- Potential Applications:**
This technology can be applied in various industries where NFTs are exchanged, such as art, gaming, and collectibles markets.
- Problems Solved:**
The technology addresses the issue of fraudulent activity in NFT exchanges, helping to protect users from scams and unauthorized transactions.
- Benefits:**
- Enhances security in NFT exchanges.
- Improves trust among users.
- Helps prevent financial losses due to fraud.
- Commercial Applications:**
Fraud detection in NFT exchanges: Enhancing security and trust in the growing NFT market.
- Prior Art:**
Readers can explore prior research on fraud detection in blockchain transactions and machine learning models for predicting fraudulent activities.
- Frequently Updated Research:**
Stay informed about advancements in machine learning algorithms for fraud detection in blockchain transactions.
- Questions about Fraud Detection in NFT Exchanges:**
1. How does the technology differentiate between legitimate and fraudulent transactions in NFT exchanges? 2. What measures are in place to ensure the accuracy and reliability of the machine learning model used for fraud prediction?
Original Abstract Submitted
Systems and methods for identifying fraudulent activity in NFT exchanges are described. Embodiments of the present disclosure obtain transaction data for non-fungible tokens (NFTs) and generate a transaction graph based on the transaction data. The transaction graph includes nodes corresponding to blockchain addresses and nodes corresponding to individual NFTs. Embodiments additionally identify a cycle of the transaction graph, predict a fraudulent activity based on the cycle using a machine learning model, and transmit an alert to a user indicating the predicted fraudulent activity.
