Enhancing Malware Detection Through Hybrid Deep Learning Techniques

Title

Enhancing Malware Detection Through Hybrid Deep Learning Techniques

Creator

Alphine, P.J.; Alapatt, Bosco Paul; George, Jossy P

Description

The detection of malware needs superior methods than basic signature detection because it remains vital to cybersecurity. This research examines malware classification through the deep learning approach by analyzing Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU) and develops a new BiGRU + CNN hybrid model. The main purpose is to achieve better detection performance through reduced numbers of false alarms. The research employs executable file feature data while implementing preprocessing methods together with fivefold cross-validation validation to establish strong model reliability. Experimental findings show CNN along with LSTM and GRU attains excellent recall values yet produces elevated erroneous positive predictions. The proposed BiGRU + CNN model delivers superiority over single-model architecture as it reaches 96.06% accuracy alongside 96.13% precision and 99.92% recall and 97.99% F1-score. The obtained results show that this integration has better malware detection capabilities thereby demonstrating its potential for cybersecurity applications. 2025 IEEE.

Source

Proceedings of 6th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2025;pp.478-483

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

BiGRU-CNN; Cybersecurity; Deep Learning; Malware Detection

Coverage

Alphine P.J., CHRIST University, School of Sciences, India; Alapatt B.P., CHRIST University, School of Sciences, India; George J.P., CHRIST University, School of Sciences, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833151175-3;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICICV64824.2025.11085589

https://www.scopus.com/pages/publications/105013072560?origin=resultslist