Machine Learning for Detecting Malware in PE Files

12/12/2022
by   Collin Connors, et al.
0

The increasing number of sophisticated malware poses a major cybersecurity threat. Portable executable (PE) files are a common vector for such malware. In this work we review and evaluate machine learning-based PE malware detection techniques. Using a large benchmark dataset, we evaluate features of PE files using the most common machine learning techniques to detect malware.

READ FULL TEXT
research
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...
research
05/18/2019

The Curious Case of Machine Learning In Malware Detection

In this paper, we argue that machine learning techniques are not ready f...
research
08/09/2023

A Feature Set of Small Size for the PDF Malware Detection

Machine learning (ML)-based malware detection systems are becoming incre...
research
12/10/2017

Improving Malware Detection Accuracy by Extracting Icon Information

Detecting PE malware files is now commonly approached using statistical ...
research
03/07/2019

Detection of Advanced Malware by Machine Learning Techniques

In today's digital world most of the anti-malware tools are signature ba...
research
10/30/2020

Being Single Has Benefits. Instance Poisoning to Deceive Malware Classifiers

The performance of a machine learning-based malware classifier depends o...
research
11/02/2018

Towards Robust Detection of Adversarial Infection Vectors: Lessons Learned in PDF Malware

Malware still constitutes a major threat in the cybersecurity landscape,...

Please sign up or login with your details

Forgot password? Click here to reset