View source for 20230075964. PHISHING MAIL GENERATOR WITH ADAPTIVE COMPLEXITY USING GENERATIVE ADVERSARIAL NETWORK simplified abstract (MASTERCARD INTERNATIONAL INCORPORATED)

=PHISHING MAIL GENERATOR WITH ADAPTIVE COMPLEXITY USING GENERATIVE ADVERSARIAL NETWORK=


==Organization Name==

[[:Category:MASTERCARD INTERNATIONAL INCORPORATED|MASTERCARD INTERNATIONAL INCORPORATED]]


[[Category:MASTERCARD INTERNATIONAL INCORPORATED]]

==Inventor(s)==

[[:Category:Alok Singh of Ghaziabad (IN)|Alok Singh of Ghaziabad (IN)]][[Category:Alok Singh of Ghaziabad (IN)]]

[[:Category:Nitish Kumar of Jamshedpur (IN)|Nitish Kumar of Jamshedpur (IN)]][[Category:Nitish Kumar of Jamshedpur (IN)]]

[[:Category:Kanishka Kayathwal of Kota (IN)|Kanishka Kayathwal of Kota (IN)]][[Category:Kanishka Kayathwal of Kota (IN)]]

==PHISHING MAIL GENERATOR WITH ADAPTIVE COMPLEXITY USING GENERATIVE ADVERSARIAL NETWORK - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20230075964 titled 'PHISHING MAIL GENERATOR WITH ADAPTIVE COMPLEXITY USING GENERATIVE ADVERSARIAL NETWORK

==Simplified Explanation==

The abstract describes a system that combines a generative adversarial network (GAN) and a reinforcement learning system to generate phishing emails with adaptive complexity. Here is a simplified explanation of the abstract:

* The system uses a trained GAN, consisting of a generator neural network and a discriminator neural network, to generate a variety of phishing emails.
* A subset of these phishing emails is selected using a reinforcement learning system that is trained on user-specific behavior.
* The selected phishing emails are then sent to a user's email account.
* Based on the user's actions and feedback to these phishing emails, the reinforcement learning system is adjusted and improved.

Potential applications of this technology:

* Cybersecurity: This technology can be used to simulate and test the effectiveness of phishing email detection systems, helping to improve cybersecurity measures.
* Training and education: The system can be used to train individuals on how to recognize and respond to phishing emails, enhancing their ability to protect themselves from cyber threats.

Problems solved by this technology:

* Adaptive complexity: By combining GAN and reinforcement learning, the system can generate phishing emails with varying levels of complexity, making them more realistic and challenging to detect.
* User-specific targeting: The reinforcement learning system is trained on user-specific behavior, allowing it to select phishing emails that are more likely to deceive a particular user.

Benefits of this technology:

* Improved cybersecurity: By generating realistic phishing emails, this technology can help identify vulnerabilities in email security systems and improve their effectiveness.
* Enhanced user awareness: By exposing users to realistic phishing emails, this technology can educate them on the tactics used by cybercriminals, making them more vigilant and less likely to fall for phishing scams.


==Original Abstract Submitted==

a generative adversarial network and a reinforcement learning system are combined to generate phishing emails with adaptive complexity. a plurality of phishing emails are obtained from a trained generative adversarial neural network, including a generator neural network and a discriminator neural network. a subset of phishing emails is selected, from the plurality of phishing emails, using a reinforcement learning system trained on user-specific behavior. one or more of the subset of phishing emails are sent to a user email account associated with a particular user. the reinforcement learning system is then adjusted based on user action feedback to the one or more of the subset of phishing emails.



[[Category:H04L29/06]]
[[Category:G06N3/08]]
[[Category:G06N3/04]]