Klassifizierung von Quelldaten durch Verarbeitung Neuronaler Netzwerke

Details

Veröffentlichungs-Nr.

EP3534283

Aktenzeichen

EP19160407

Anmeldetag

1. März 2019

Veröffentlichung

19. Oktober 2022

Erteilung

19. Oktober 2022

Rechtsraum

EP

IPC

G06F21/56

Abstract

Example techniques described herein determine a classification of a variable-length source data such as an executable code. A neural network system that includes a convolution filter, a recurrent neural network, and a fully connected layer can be configured in a computing device to classify executable code. The neural network system can receive executable code of variable length and reduce its dimensionality by generating a variable-length sequence of features extracted from the executable code. The sequence of features is filtered, and applied to one or more recurrent neural networks and to a neural network. The output of the neural network classifies the data. Other disclosed systems include a system for reducing the dimensionality of command line input using a recurrent neural network. The reduced dimensionality of command line input may be classified using the disclosed neural network systems.

Anmelder

Firma

CrowdStrike, Inc.

Land

🇺🇸 USA

Fachgebiete