Ransomware Detection with Deep Neural Networks

Matan Davidian, Natalia Vanetik, Michael Kiperberg

2022

Abstract

The number of reported malware and their average identification time increases each year, thus increasing the mitigation cost. Static analysis techniques cannot reliably detect polymorphic and metamorphic malware, while dynamic analysis is more effective in detecting advanced malware, especially when the analysis is performed using machine-learning techniques. This paper presents a novel approach for the detection of ransomware, a particular type of malware. The approach uses word embeddings to represent system call features and deep neural networks such as Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM). The evaluation, performed on two datasets, shows that the described approach achieves a detection rate of over 99% for ransomware samples.

Paper Citation

in Harvard Style

Davidian M., Vanetik N. and Kiperberg M. (2022). Ransomware Detection with Deep Neural Networks. In Proceedings of the 8th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP, ISBN 978-989-758-553-1, pages 656-663. DOI: 10.5220/0011008000003120

in Bibtex Style

@conference{icissp22,
author={Matan Davidian and Natalia Vanetik and Michael Kiperberg},
title={Ransomware Detection with Deep Neural Networks},
booktitle={Proceedings of the 8th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,},
year={2022},
pages={656-663},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011008000003120},
isbn={978-989-758-553-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,
TI - Ransomware Detection with Deep Neural Networks
SN - 978-989-758-553-1
AU - Davidian M.
AU - Vanetik N.
AU - Kiperberg M.
PY - 2022
SP - 656
EP - 663
DO - 10.5220/0011008000003120