Palo Alto Networks, Inc. (20240330960). DETECTING FRAUDULENT E-COMMERCE WEBSITES simplified abstract
Contents
DETECTING FRAUDULENT E-COMMERCE WEBSITES
Organization Name
Inventor(s)
Marzieh Bitaab of Chandler AZ (US)
Seokkyung Chung of Sunnyvale CA (US)
DETECTING FRAUDULENT E-COMMERCE WEBSITES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240330960 titled 'DETECTING FRAUDULENT E-COMMERCE WEBSITES
The abstract of the patent application describes techniques for identifying fraudulent e-commerce websites (FCWs). A model trained on tokens extracted from both benign shopping sites and previously identified FCWs is used to evaluate the content associated with a candidate URL. Remedial action is taken based on the model's determination.
- The innovation involves using a trained model to assess the content of e-commerce websites and identify potential fraud.
- Tokens extracted from both legitimate and fraudulent sites are used to train the model, enhancing its accuracy in detecting fraudulent activity.
- Remedial actions are taken in response to the model's evaluation, helping to protect users from potential scams and fraud.
Potential Applications: This technology can be applied in the e-commerce industry to enhance security measures and protect consumers from fraudulent websites. It can also be utilized by regulatory bodies and law enforcement agencies to identify and take down illegal online operations.
Problems Solved: This technology addresses the issue of fraudulent e-commerce websites deceiving consumers and engaging in illegal activities. It helps in maintaining the integrity of online shopping platforms and safeguarding users from financial losses.
Benefits: Enhanced security for online shoppers. Improved detection and prevention of fraudulent activities in the e-commerce sector. Increased trust and confidence in online transactions.
Commercial Applications: The technology can be integrated into e-commerce platforms to provide an additional layer of security for users. It can be marketed to online retailers, payment processors, and regulatory agencies to enhance fraud detection capabilities in the digital marketplace.
Questions about the technology: 1. How does the trained model differentiate between tokens from benign sites and those from fraudulent sites? 2. What types of remedial actions are typically performed in response to the model's determination?
Frequently Updated Research: Stay informed about the latest advancements in fraud detection technologies and methodologies to ensure the continued effectiveness of this innovation.
Original Abstract Submitted
techniques for identifying fraudulent e-commerce websites (fcws) are disclosed. a candidate url is received. a model, previously trained (e.g., using a first set of tokens extracted from a set of benign shopping sites and a second set of tokens extracted a set of previously identified fcws) is used to evaluate content associated with the url. a remedial action is performed in response to the determination.
