DeepAI AI Chat
Log In Sign Up

Improving Malware Detection Accuracy by Extracting Icon Information

12/10/2017
by   Pedro Silva, et al.
Cylance
University of California, Irvine
0

Detecting PE malware files is now commonly approached using statistical and machine learning models. While these models commonly use features extracted from the structure of PE files, we propose that icons from these files can also help better predict malware. We propose an innovative machine learning approach to extract information from icons. Our proposed approach consists of two steps: 1) extracting icon features using summary statics, histogram of gradients (HOG), and a convolutional autoencoder, 2) clustering icons based on the extracted icon features. Using publicly available data and by using machine learning experiments, we show our proposed icon clusters significantly boost the efficacy of malware prediction models. In particular, our experiments show an average accuracy increase of 10 prediction model.

READ FULL TEXT
12/12/2022

Machine Learning for Detecting Malware in PE Files

The increasing number of sophisticated malware poses a major cybersecuri...
11/22/2021

A Comparison of State-of-the-Art Techniques for Generating Adversarial Malware Binaries

We consider the problem of generating adversarial malware by a cyber-att...
04/12/2018

EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models

This paper describes EMBER: a labeled benchmark dataset for training mac...
08/01/2019

KiloGrams: Very Large N-Grams for Malware Classification

N-grams have been a common tool for information retrieval and machine le...
09/21/2019

Dynamic data fusion using multi-input models for malware classification

Criminals use malware to disrupt cyber-systems. The number of these malw...