Proceedings of the International Conference on Responsible, Risk-aware, and Regulated AI (RRRAI 2026)

International Conference on Responsible, Risk-aware, and Regulated AI (RRRAI 2026)

📍Pune, Maharashtra, India🗓️ 3-4 April 2026
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Malware Detection Using NLP-based Hybrid Feature Analysis: A Comprehensive Review

Authors
Manoj D. Shelar1, Monali R. Devkar2, Neha S. Gadhave2, Sayali S. Jadhav2, *, Tanuja R. More2
1Assistant Professor, (Computer Engineering), Vidya Pratishthan’s Kamalnayan Bajaj Institute of Engineering and Technology, Baramati, SPPU Pune, Maharashtra, India
2Student, (Computer Engineering), Vidya Pratishthan’s Kamalnayan Bajaj Institute of Engineering and Technology, Baramati, SPPU Pune, Maharashtra, India
*Corresponding author. Email: ssjadhav715@gmail.com
Corresponding Author
Sayali S. Jadhav
Available Online 14 July 2026.
DOI
10.2991/978-94-6239-723-1_40How to use a DOI?
Keywords
Malware Analysis; Cybersecurity; Deep Learning; NLP; Hybrid Model; Attention Mechanism
Abstract

The complex modern malware cannot be recognised by a typical detection system signature based detection and single layer analysis detection will not be able to work. This paper suggests a hybrid malware detection model that statically extracts and employs multiple types of features, such as printable strings, API-level structural metadata, and packing tool signatures. Each stream is encoded independently so as not to lose any of the features semantic information which is then combined through an attention-based LSTM. The model effectively addresses key challenges like the issue of feature dominance, sensitivity to sequence alignment, and the fusion of imbalanced features. Use of NLP based multi-feature approach for detection of malware and malware based attack in large scale dataset is shown empirically. This proves to be more robust against obfuscated and zero-day attacks as compared to model based on single feature.

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.

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Volume Title
Proceedings of the International Conference on Responsible, Risk-aware, and Regulated AI (RRRAI 2026)
Series
Advances in Intelligent Systems Research
Publication Date
14 July 2026
ISBN
978-94-6239-723-1
ISSN
1951-6851
DOI
10.2991/978-94-6239-723-1_40How 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  - Manoj D. Shelar
AU  - Monali R. Devkar
AU  - Neha S. Gadhave
AU  - Sayali S. Jadhav
AU  - Tanuja R. More
PY  - 2026
DA  - 2026/07/14
TI  - Malware Detection Using NLP-based Hybrid Feature Analysis: A Comprehensive Review
BT  - Proceedings of the International Conference on Responsible, Risk-aware, and Regulated AI (RRRAI 2026)
PB  - Atlantis Press
SP  - 453
EP  - 462
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-723-1_40
DO  - 10.2991/978-94-6239-723-1_40
ID  - Shelar2026
ER  -