Suspicious filename detection using a deep learning neural network

Abstract

Disclosed herein is a machine learning-based approach to detect suspiciously named processes. When malware executes on a networking device, such as a laptop or desktop computer, the malware may create a copy of itself, assign the copy a process name consisting of random characters, and store the copy in a directory of the networking device. As characters of words in a given language follow patterns and rules, the presence of each character is not equally likely. In contrast, characters in random sequences have an equal likelihood of being present. In some implementations disclosed herein, a character-level recurrent neural network (RNN) is trained to distinguish between randomly generated filenames from those created by an user and thus, identify malware attacks. In some implementations, a character-level RNN is configured to classify filenames as malicious or benign.