Open Access

ARTICLE

An Attention-Based Approach to Enhance the Detection and Classification of Android Malware

by Abdallah Ghourabi^*

Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka, 72388, Saudi Arabia

* Corresponding Author: Abdallah Ghourabi. Email:

Computers, Materials & Continua 2024, 80(2), 2743-2760. https://doi.org/10.32604/cmc.2024.053163

Received 26 April 2024; Accepted 07 June 2024; Issue published 15 August 2024

Abstract

The dominance of Android in the global mobile market and the open development characteristics of this platform have resulted in a significant increase in malware. These malicious applications have become a serious concern to the security of Android systems. To address this problem, researchers have proposed several machine-learning models to detect and classify Android malware based on analyzing features extracted from Android samples. However, most existing studies have focused on the classification task and overlooked the feature selection process, which is crucial to reduce the training time and maintain or improve the classification results. The current paper proposes a new Android malware detection and classification approach that identifies the most important features to improve classification performance and reduce training time. The proposed approach consists of two main steps. First, a feature selection method based on the Attention mechanism is used to select the most important features. Then, an optimized Light Gradient Boosting Machine (LightGBM) classifier is applied to classify the Android samples and identify the malware. The feature selection method proposed in this paper is to integrate an Attention layer into a multilayer perceptron neural network. The role of the Attention layer is to compute the weighted values of each feature based on its importance for the classification process. Experimental evaluation of the approach has shown that combining the Attention-based technique with an optimized classification algorithm for Android malware detection has improved the accuracy from 98.64% to 98.71% while reducing the training time from 80 to 28 s.

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Keywords

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