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Authors: Radu Mihalache ; Dragoș Gavriluț and Dan Anton

Affiliation: Bitdefender Laboratory, Faculty of Computer Science, ”Al.I. Cuza” University, Iași, Romania

Keyword(s): Malware, Threat Detection, Neural Network, Static Features, Dynamic Features.

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.

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Paper citation in several formats:
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; ISSN 2184-433X, SciTePress, pages 226-234. DOI: 10.5220/0012316800003636

@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},
issn={2184-433X},
}

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
IS - 2184-433X
AU - Mihalache, R.
AU - Gavriluț, D.
AU - Anton, D.
PY - 2024
SP - 226
EP - 234
DO - 10.5220/0012316800003636
PB - SciTePress

- Science and Technology Publications, Lda.
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