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Authors: Matan Davidian ; Natalia Vanetik and Michael Kiperberg

Affiliation: Shamoon College of Engineering, Beer Sheva, Israel

Keyword(s): Dynamic Analysis, Malware Detection, Neural Networks, Ransomware.

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.

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Paper citation in several formats:
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 - ICISSP; ISBN 978-989-758-553-1; ISSN 2184-4356, SciTePress, pages 656-663. DOI: 10.5220/0011008000003120

@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 - ICISSP},
year={2022},
pages={656-663},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011008000003120},
isbn={978-989-758-553-1},
issn={2184-4356},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Information Systems Security and Privacy - ICISSP
TI - Ransomware Detection with Deep Neural Networks
SN - 978-989-758-553-1
IS - 2184-4356
AU - Davidian, M.
AU - Vanetik, N.
AU - Kiperberg, M.
PY - 2022
SP - 656
EP - 663
DO - 10.5220/0011008000003120
PB - SciTePress

- Science and Technology Publications, Lda.
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