Paper Info
Title
The rise of machine learning for detection and classification of malware: Research developments, trends and challenges.
Abstract
The struggle between security analysts and malware developers is a never-ending battle with the complexity of malware changing as quickly as innovation grows. Current state-of-the-art research focus on the development and application of machine learning techniques for malware detection due to its ability to keep pace with malware evolution. This survey aims at providing a systematic and detailed overview of machine learning techniques for malware detection and in particular, deep learning techniques. The main contributions of the paper are: (1) it provides a complete description of the methods and features in a traditional machine learning workflow for malware detection and classification, (2) it explores the challenges and limitations of traditional machine learning and (3) it analyzes recent trends and developments in the field with special emphasis on deep learning approaches. Furthermore, (4) it presents the research issues and unsolved challenges of the state-of-the-art techniques and (5) it discusses the new directions of research. The survey helps researchers to have an understanding of the malware detection field and of the new developments and directions of research explored by the scientific community to tackle the problem.
Year
DOI
Venue
2020
10.1016/j.jnca.2019.102526
Journal of Network and Computer Applications
Keywords
Field
DocType
Malware detection,Feature engineering,Machine learning,Deep learning,Multimodal learning
Malware research,Pace,Computer science,Artificial intelligence,Deep learning,Malware,Workflow,Machine learning
Journal
Volume
ISSN
Citations 
153
1084-8045
10
PageRank 
References 
Authors
0.57
0
3
Name
Order
Citations
PageRank
Daniel Gibert1264.29
Carles Mateu27914.22
Jordi Planes348631.38