Deep Neural Networks for Android Malware Detection

Abhilash Hota, Paul Irolla

2019

Abstract

In this paper we present a study of the application of deep neural networks to the problem of pattern matching in Android malware detection. Over the last few years malware have been proliferating and malware authors keep developing new techniques to bypass existing detection methods. Machine learning techniques in general and deep neural networks in particular have been very successful in recent years in a variety of classification tasks. We study various deep neural networks as potential solutions for pattern matching in malware detection systems. The effectiveness of the different architectures is compared and judged as a potential replacement for traditional approaches to malware detection in Android systems.

Paper Citation

in Harvard Style

Hota A. and Irolla P. (2019). Deep Neural Networks for Android Malware Detection.In Proceedings of the 5th International Conference on Information Systems Security and Privacy - Volume 1: ForSE, ISBN 978-989-758-359-9, pages 657-663. DOI: 10.5220/0007617606570663

in Bibtex Style

@conference{forse19,
author={Abhilash Hota and Paul Irolla},
title={Deep Neural Networks for Android Malware Detection},
booktitle={Proceedings of the 5th International Conference on Information Systems Security and Privacy - Volume 1: ForSE,},
year={2019},
pages={657-663},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007617606570663},
isbn={978-989-758-359-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 5th International Conference on Information Systems Security and Privacy - Volume 1: ForSE,
TI - Deep Neural Networks for Android Malware Detection
SN - 978-989-758-359-9
AU - Hota A.
AU - Irolla P.
PY - 2019
SP - 657
EP - 663
DO - 10.5220/0007617606570663