20240020960. Neural Network Host Platform for Detecting Anomalies in Cybersecurity Modules simplified abstract (Proofpoint, Inc.)

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Neural Network Host Platform for Detecting Anomalies in Cybersecurity Modules

Organization Name

Proofpoint, Inc.

Inventor(s)

Adam Jason of Zelienople PA (US)

Neural Network Host Platform for Detecting Anomalies in Cybersecurity Modules - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240020960 titled 'Neural Network Host Platform for Detecting Anomalies in Cybersecurity Modules

Simplified Explanation

The abstract of this patent application describes a method for anomaly detection in cybersecurity training modules. A computing platform receives information defining a training module and captures multiple screenshots of different permutations of the module. These screenshots are then inputted into an auto-encoder, which outputs a reconstruction error value. An outlier detection algorithm is executed on the reconstruction error value to identify any outlier permutations of the training module. A user interface is generated with information identifying the outlier permutation and sent to user devices.

  • The computing platform receives information defining a cybersecurity training module.
  • Multiple screenshots of different permutations of the training module are captured.
  • The screenshots are inputted into an auto-encoder.
  • The auto-encoder outputs a reconstruction error value.
  • An outlier detection algorithm is executed on the reconstruction error value.
  • Outlier permutations of the training module are identified.
  • A user interface is generated with information about the outlier permutation.
  • The user interface is sent to user devices.

Potential applications of this technology:

  • Anomaly detection in cybersecurity training modules.
  • Identifying outlier permutations of training modules.

Problems solved by this technology:

  • Detecting anomalies or outliers in cybersecurity training modules.
  • Providing a method to identify and address potential issues or vulnerabilities in training modules.

Benefits of this technology:

  • Improved cybersecurity training by identifying and addressing outlier permutations.
  • Enhanced detection of anomalies or vulnerabilities in training modules.
  • Efficient identification of potential issues in training modules.


Original Abstract Submitted

aspects of the disclosure relate to anomaly detection in cybersecurity training modules. a computing platform may receive information defining a training module. the computing platform may capture a plurality of screenshots corresponding to different permutations of the training module. the computing platform may input, into an auto-encoder, the plurality of screenshots corresponding to the different permutations of the training module, wherein inputting the plurality of screenshots corresponding to the different permutations of the training module causes the auto-encoder to output a reconstruction error value. the computing platform may execute an outlier detection algorithm on the reconstruction error value, which may cause the computing platform to identify an outlier permutation of the training module. the computing platform may generate a user interface comprising information identifying the outlier permutation of the training module. the computing platform may send the user interface to at least one user device.

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