US 2026/0064628 A1 · REDUCED-SIZE FILE TYPE CLASSIFICATION WITH DEEP LEARNING

Abstract

Files are classified by file type based on reduced-size representations of the file contents (e.g., truncated versions of files) using a trained convolutional neural network (CNN). The CNN architecture includes a hidden layer comprising a Kolmogorov-Arnold Network (KAN) layer in lieu of a traditional fully connected layer. Training of the CNN employs a cost function that combines cross-entropy loss and contrastive loss for evaluating CNN performance. Training of the CNN is also incremental-when training a CNN for the task of classifying reduced-size files, the CNN is first trained on a training dataset comprising files of their original sizes. Once this initial phase of training is complete, the trained CNN is fine-tuned as a result of one or more additional phases of training, where each additional training phase uses a training dataset comprising reduced-size (e.g., truncated) versions of the files.

Claims20

Dependent claims

A method comprising:

based on detecting a first file, reducing a size of the first file to a designated size to generate a reduced-size file;
predicting a type of the first file based on inputting the reduced-size file into a convolutional neural network (CNN),
wherein the CNN has been trained to classify reduced-size files of the designated size as corresponding to one of a plurality of file types,
wherein the CNN comprises a first hidden layer corresponding to a Kolmogorov-Arnold Network (KAN) layer;
and
indicating the predicted type of the first file based on an output of the CNN.

3 more elements

One or more non-transitory machine-readable media having program code stored thereon, the program code comprising instructions to:

based on designation of a first file for classification, truncate the first file to a designated size to generate a truncated file;
predict a type of the first file based on input of the truncated file into a convolutional neural network (CNN),
wherein the CNN has been trained to classify truncated files of the designated size as corresponding to one of a plurality of file types,
wherein the CNN comprises a first hidden layer corresponding to a Kolmogorov-Arnold Network (KAN) layer;
and
indicate the predicted type of the first file based on an output of the CNN.

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,
reduce a size of a first file indicated for classification to a designated size to generate a reduced-size file;
predict a type of the first file based on input of the reduced-size file into a convolutional neural network (CNN),
wherein the CNN has been trained to classify reduced-size files of the designated size as corresponding to one of a plurality of file types,
wherein the CNN comprises a first hidden layer corresponding to a Kolmogorov-Arnold Network (KAN) layer;
and
indicate the predicted type of the first file based on an output of the CNN.

6 more elements

Dependencies are inferred from the claim text.

Reduced-size file type classification with deep learning

Life of this patent

Reduced-size file type classification with deep learning

Life of this patent

Abstract

Claims20