AI-Driven Ransomware Detection and Classification for Improved Cyber Defense

DOI

10.2991/978-94-6463-858-5_92How to use a DOI?

Keywords

Ransomware; MACHINE LEARNING (ML); DEEP LEARNING (DL); Cybersecurity; Random Forest (RF); XGBoost Classifier; Sequential Model; Executable Files (.exe); Memory Allocation

Abstract

Ransomware poses major threats to cybersecurity, disrupting networks, applications, and data centers across various sectors. Traditional defenses fail against sophisticated attacks, necessitating advanced solutions. We propose a feature selection-based framework using deep learning to enhance ransomware detection. Three models—Random Forest, Sequential, and XGBoost—were evaluated on a dataset of ransomware files, with XGBoost achieving peak accuracy.

Copyright

© 2025 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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TY  - CONF
AU  - Pilaka Anusha
AU  - Chigurupati Tanmayi
AU  - Inturi Bindu Vahini
AU  - Boddupalli Guna Priya
AU  - Pasunuti Lahari
PY  - 2025
DA  - 2025/11/04
TI  - AI-Driven Ransomware Detection and Classification for Improved Cyber Defense
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 1108
EP  - 1117
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_92
DO  - 10.2991/978-94-6463-858-5_92
ID  - Anusha2025
ER  -