An ensemble deep learning classifier stacked with fuzzy ARTMAP for malware detection

Shing, Chiang Tan and Mohammed Al-Andoli, Mohammed Nasser and Kok, Swee Lim and Pey, Yun Goh and Chee, Peng Lim (2023) An ensemble deep learning classifier stacked with fuzzy ARTMAP for malware detection. Journal of Intelligent & Fuzzy Systems, 44 (6). pp. 10477-10493. ISSN 1875-8967

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Abstract

Malicious software, or malware, has posed serious and evolving security threats to Internet users. Many antimalware software packages and tools have been developed to protect legitimate users from these threats. However, legacy anti-malware methods are confronted with millions of potential malicious programs. To combat these threats, intelligent anti-malware systems utilizing machine learning (ML) models are useful. However, most ML models have limitations in performance since the training depth is usually limited. The emergence of Deep Learning (DL) models allow more training possibilities and improvement in performance. DL models often use gradient descent optimization, i.e., the Back-Propagation (BP) algorithm; therefore, their training and optimization procedures suffer from local sub-optimal solutions. In addition, DL-based malware detection methods often entail single classifiers. Ensemble learning overcomes the shortcomings of individual techniques by consolidating their strengths to improve the performance. In this paper, we propose an ensemble DL classifier stacked with the Fuzzy ARTMAP (FAM) model for malware detection. The stacked ensemble method uses several heterogeneous deep neural networks as the base learners. During the training and optimization process, these base learners adopt a hybrid BP and Particle Swarm Optimization algorithm to combine both local and global optimization capabilities for identifying optimal features and improving the classification performance. FAM is selected as a meta-learner to effectively train and combine the outputs of the base learners and achieve robust and accurate classification. A series of empirical studies with different benchmark data sets is conducted. The results ascertain that the proposed ensemble method is effective and efficient, outperforming many other compared methods.

Item Type:	Article
Uncontrolled Keywords:	Ensemble learning, Fuzzy ARTMAP, Deep learning, Malware detection, Particle swarm optimization, Backpropagation algorithm
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	11 Apr 2025 11:58
Last Modified:	11 Apr 2025 11:58
URI:	http://eprints.utem.edu.my/id/eprint/28667
Statistic Details:	View Download Statistic

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