Real-Time Deep Learning-Based Malware Detection Using Static and Dynamic Features

Radu Mihalache, Dragoș Gavriluț, Dan Anton

2024

Abstract

Cyber-security industry has been the home of various machine learning approaches meant to be more proactive when it comes to new threats. In time, as security solutions matured, so did the way in which artificial intelligence algorithms are being used for specific contexts. In particular, static and dynamic analysis of a threat determines certain characteristics of an artificial intelligence algorithm (such as inference speed, memory usage) used for threat detection. While from a product point of view, static and dynamic analysis of a threat target separate product features such as protection for static analysis and detection for dynamic analysis, the feature sets derived from analyzing threats in those two scenarios (static and dynamic analysis) are complementary and could improve the accuracy of a model if used together. The current paper focuses on a multi-layered approach that takes into consideration both static and dynamic analysis of a threat.

Paper Citation

in Harvard Style

Mihalache R., Gavriluț D. and Anton D. (2024). Real-Time Deep Learning-Based Malware Detection Using Static and Dynamic Features. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 226-234. DOI: 10.5220/0012316800003636

in Bibtex Style

@conference{icaart24,
author={Radu Mihalache and Dragoș Gavriluț and Dan Anton},
title={Real-Time Deep Learning-Based Malware Detection Using Static and Dynamic Features},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={226-234},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012316800003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Real-Time Deep Learning-Based Malware Detection Using Static and Dynamic Features
SN - 978-989-758-680-4
AU - Mihalache R.
AU - Gavriluț D.
AU - Anton D.
PY - 2024
SP - 226
EP - 234
DO - 10.5220/0012316800003636
PB - SciTePress