Malware Detection with AI

In this paper, we will present two existing approaches that utilize machine learning techniques for dynamic analysis, as well as an approach that employs deep learning. Additionally, we will propose an enhancement to a previously studied method. For the dynamic analysis of malware, we review various current research directions, focusing on several machine learning techniques used in this context, though without providing experimental results. The aim is to offer the reader a concise introduction to the different techniques and algorithmic formulations. Similarly, the approach involving deep learning will be discussed. Finally, we introduce a modification to an idea previously proposed in the literature and analyze the differences it brings.

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Publication Details

Published
2024-09-18
DOI
https://doi.org/10.1109/argencon62399.2024.10735932
Primary Topic
Advanced Malware Detection Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

Malware Detection with AI

Hugo D. Scolnik, Khalil Moriello
Advanced Malware Detection Techniques
article

Malware Detection with AI

Hugo D. Scolnik, Khalil Moriello
article en

Abstract

In this paper, we will present two existing approaches that utilize machine learning techniques for dynamic analysis, as well as an approach that employs deep learning. Additionally, we will propose an enhancement to a previously studied method. For the dynamic analysis of malware, we review various current research directions, focusing on several machine learning techniques used in this context, though without providing experimental results. The aim is to offer the reader a concise introduction to the different techniques and algorithmic formulations. Similarly, the approach involving deep learning will be discussed. Finally, we introduce a modification to an idea previously proposed in the literature and analyze the differences it brings.

Universidad de Buenos Aires (AR)
Openalex Percentile: Top 78%
Advanced Malware Detection Techniques
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