Proceedings of the International Conference on Advances in Computing Technology and Artificial Intelligence (COMPUTATIA 2026)

International Conference on Advances in Computing Technology and Artificial Intelligence (COMPUTATIA 2026)

📍Jaipur, India🗓️ 23-24 March 2026
<Previous Article In Volume
Next Article In Volume>

A Hybrid Static–Dynamic Deep Learning Approach for Malware Classification

Authors
Yash Agarwal1, *, Shikhar Srivastava1, Sanket Jain1, Nisha Pal1, Sanjay Khakhil1
1Galgotias College of Engineering and Technology affiliated to Dr A. P. J. Abdul Kalam Technical University, Lucknow, India
*Corresponding author. Email: 12.yashag@gmail.com
Corresponding Author
Yash Agarwal
Available Online 25 June 2026.
DOI
10.2991/978-94-6239-713-2_21How to use a DOI?
Keywords
Malware Detection; Hybrid Analysis; API Call Analysis
Abstract

The rapid proliferation of malware and the sophistication of evasion strategies have diminished the effectiveness of traditional signature-based detection approaches. While static analysis is computationally efficient, its susceptibility to evasion remains a challenge; dynamic analysis offers behavioral knowledge at a higher computational cost. In this paper, we introduce a hybrid deep learning model that combines Printable String Information (PSI) derived from static analysis with dynamic API call sequences to improve malware classification accuracy. We use a multi-input neural network model to classify malware using the proposed feature set. We evaluated our proposed model using the BODMAS dataset and found that our proposed model achieves an accuracy of 98.32%, which is superior to individual static (92.4%) and dynamic (94.1%) models.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the International Conference on Advances in Computing Technology and Artificial Intelligence (COMPUTATIA 2026)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
25 June 2026
ISBN
978-94-6239-713-2
ISSN
2589-4919
DOI
10.2991/978-94-6239-713-2_21How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Yash Agarwal
AU  - Shikhar Srivastava
AU  - Sanket Jain
AU  - Nisha Pal
AU  - Sanjay Khakhil
PY  - 2026
DA  - 2026/06/25
TI  - A Hybrid Static–Dynamic Deep Learning Approach for Malware Classification
BT  - Proceedings of the International Conference on Advances in Computing Technology and Artificial Intelligence (COMPUTATIA 2026)
PB  - Atlantis Press
SP  - 292
EP  - 301
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-713-2_21
DO  - 10.2991/978-94-6239-713-2_21
ID  - Agarwal2026
ER  -