Open Access

ARTICLE

Advanced Techniques for Dynamic Malware Detection and Classification in Digital Security Using Deep Learning

Taher Alzahrani^*

Information System Department, College of Computer and Information Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11673, Saudi Arabia

* Corresponding Author: Taher Alzahrani. Email:

(This article belongs to the Special Issue: Challenges and Innovations in Multimedia Encryption and Information Security)

Computers, Materials & Continua 2025, 83(3), 4575-4606. https://doi.org/10.32604/cmc.2025.063448

Received 15 January 2025; Accepted 25 March 2025; Issue published 19 May 2025

Abstract

The rapid evolution of malware presents a critical cybersecurity challenge, rendering traditional signature-based detection methods ineffective against novel variants. This growing threat affects individuals, organizations, and governments, highlighting the urgent need for robust malware detection mechanisms. Conventional machine learning-based approaches rely on static and dynamic malware analysis and often struggle to detect previously unseen threats due to their dependency on predefined signatures. Although machine learning algorithms (MLAs) offer promising detection capabilities, their reliance on extensive feature engineering limits real-time applicability. Deep learning techniques mitigate this issue by automating feature extraction but may introduce computational overhead, affecting deployment efficiency. This research evaluates classical MLAs and deep learning models to enhance malware detection performance across diverse datasets. The proposed approach integrates a novel text and image-based detection framework, employing an optimized Support Vector Machine (SVM) for textual data analysis and EfficientNet-B0 for image-based malware classification. Experimental analysis, conducted across multiple train-test splits over varying timescales, demonstrates 99.97% accuracy on textual datasets using SVM and 96.7% accuracy on image-based datasets with EfficientNet-B0, significantly improving zero-day malware detection. Furthermore, a comparative analysis with existing competitive techniques, such as Random Forest, XGBoost, and CNN-based (Convolutional Neural Network) classifiers, highlights the superior performance of the proposed model in terms of accuracy, efficiency, and robustness.

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Keywords

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