US 2024/0403570 A1 · CREDENTIAL DETECTION FOR DATA LOSS PREVENTION WITH ONE-DIMENSIONAL CONVOLUTIONAL NEURAL NETWORKS

Abstract

A trained one-dimensional convolutional neural network (1D CNN) efficiently detects credentials that allow access to sensitive data across an organization. The 1D CNN has a lightweight architecture with one or more one-dimensional convolutional layers that capture semantic context of text data and a one-hot encoding embedding layer that takes unprocessed characters from documents as input. Lightweight architecture of the 1D CNN allows for high volume, fast detection of credentials for data loss prevention. The 1D CNN is trained on documents augmented with natural language processing techniques including token replacement, machine translation, token rearrangement, and text summarization.

Claims20

Dependent claims

A method comprising:

training a convolutional neural network to detect credentials in documents, wherein training the convolutional neural network comprises,
identifying a plurality of documents and a plurality of labels indicating whether each of the plurality of documents comprises one or more credentials;
augmenting the plurality of documents with natural language processing to generate a plurality of augmented documents at least comprising the plurality of documents;
and
training the convolutional neural network with the plurality of augmented documents and the plurality of labels;
and
deploying the trained convolutional neural network to detect credentials for data loss prevention.

4 more elements

A non-transitory machine-readable medium having program code stored thereon, the program code comprising instructions to:

detect credentials in documents for data loss prevention with a machine learning model, wherein the instructions to detect credentials comprise instructions to,
identify one or more documents comprising potentially sensitive data;
and
input the one or more documents into the machine learning model to obtain one or more verdicts indicating the presence of credentials in corresponding documents of the one or more documents as outputs, wherein the machine learning model was trained on documents augmented with natural language processing and labels indicating whether each of the documents comprises credentials;
and
indicate at least a subset of the one or more documents in association with the one or more verdicts indicating the presence of credentials in corresponding documents.

3 more elements

An apparatus comprising:

a processor;
and
a machine-readable medium having instructions stored thereon that are executable by the processor to cause the apparatus to,
identify a plurality of documents and a plurality of labels indicating whether each of the plurality of documents comprises credentials;
augment the plurality of documents with natural language processing to obtain a plurality of augmented documents at least comprising the plurality of documents;
train a convolutional neural network to detect credentials in documents based, at least in part, on the plurality of augmented documents and corresponding ones of the plurality of labels;
and
deploy the trained convolution neural network to detect credentials in potential sensitive documents for data loss prevention.

5 more elements

Dependencies are inferred from the claim text.

Credential detection for data loss prevention with one-dimensional convolutional neural networks

Life of this patent

Credential detection for data loss prevention with one-dimensional convolutional neural networks

Life of this patent

Abstract

Claims20