View source for Adobe Inc. (20240303662). FRAUD DETECTION IN NFT EXCHANGES simplified abstract

=FRAUD DETECTION IN NFT EXCHANGES=


==Organization Name==

[[:Category:Adobe Inc.|Adobe Inc.]]


[[Category:Adobe Inc.]]

==Inventor(s)==

[[:Category:Raunak Shah of Wagholi (IN)|Raunak Shah of Wagholi (IN)]][[Category:Raunak Shah of Wagholi (IN)]]

[[:Category:Harshvardhan . of Bokaro Steel City (IN)|Harshvardhan . of Bokaro Steel City (IN)]][[Category:Harshvardhan . of Bokaro Steel City (IN)]]

[[:Category:Mohit Kumar of Bathinda (IN)|Mohit Kumar of Bathinda (IN)]][[Category:Mohit Kumar of Bathinda (IN)]]

[[:Category:Shambhavi Pardhi of Balaghat (IN)|Shambhavi Pardhi of Balaghat (IN)]][[Category:Shambhavi Pardhi of Balaghat (IN)]]

[[:Category:Alakh Dixit of Indore (IN)|Alakh Dixit of Indore (IN)]][[Category:Alakh Dixit of Indore (IN)]]

[[:Category:Shaddy Garg of Baghapurana (IN)|Shaddy Garg of Baghapurana (IN)]][[Category:Shaddy Garg of Baghapurana (IN)]]

[[:Category:Shiv Kumar Saini of Bangalore (IN)|Shiv Kumar Saini of Bangalore (IN)]][[Category:Shiv Kumar Saini of Bangalore (IN)]]

[[:Category:Ramasuri Narayanam of Bangalore (IN)|Ramasuri Narayanam of Bangalore (IN)]][[Category:Ramasuri Narayanam of Bangalore (IN)]]

==FRAUD DETECTION IN NFT EXCHANGES - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240303662 titled 'FRAUD DETECTION IN NFT EXCHANGES

The patent application describes systems and methods for identifying fraudulent activity in NFT exchanges by analyzing transaction data and generating a transaction graph.

* Transaction data for NFTs is obtained and used to create a transaction graph with nodes representing blockchain addresses and individual NFTs.
* A cycle in the transaction graph is identified, and fraudulent activity is predicted using a machine learning model.
* An alert is then sent to the user to notify them of the predicted fraudulent activity.

Potential Applications:
- Enhancing security in NFT exchanges
- Preventing fraudulent transactions in the blockchain space

Problems Solved:
- Detecting and preventing fraudulent activity in NFT exchanges
- Improving trust and reliability in blockchain transactions

Benefits:
- Increased security for NFT holders
- Reduced risk of financial loss due to fraudulent activity

Commercial Applications:
Title: "Enhancing Security in NFT Exchanges: Commercial Applications and Market Implications"
This technology could be utilized by NFT marketplaces, cryptocurrency exchanges, and financial institutions to enhance security measures and protect users from fraudulent activities.

Prior Art:
Readers can explore prior art related to fraud detection in blockchain transactions, machine learning models for predicting fraudulent activities, and security measures in NFT exchanges.

Frequently Updated Research:
Stay informed about the latest advancements in fraud detection technologies, machine learning algorithms for predicting fraudulent activities, and security protocols in blockchain transactions.

Questions about Fraud Detection in NFT Exchanges:
1. How does the machine learning model predict fraudulent activity based on the transaction graph?
- The machine learning model analyzes patterns in the transaction graph to identify potential fraudulent cycles and activities.
2. What are some common indicators of fraudulent activity in NFT exchanges?
- Common indicators include unusual transaction patterns, suspicious wallet addresses, and unexpected changes in ownership of NFTs.


==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.



[[Category:G06Q20/40]]
[[Category:G06Q20/38]]
[[Category:CPC_G06Q20/4016]]