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An ensemble deep learning classifier stacked with fuzzy ARTMAP for malware detection
journal contribution
posted on 2023-07-21, 03:28 authored by Mohammed Nasser Al-Andoli, Shing Chiang Tan, Kok Swee Sim, Pey Yun Goh, Chee Peng LimChee Peng LimMalicious software, or malware, has posed serious and evolving security threats to Internet users. Many anti-malware 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.
History
Journal
JOURNAL OF INTELLIGENT & FUZZY SYSTEMSVolume
44Pagination
10477-10493Location
Amsterdam, The NetherlandsPublisher DOI
ISSN
1064-1246eISSN
1875-8967Language
EnglishPublication classification
C1 Refereed article in a scholarly journalIssue
6Publisher
IOS PRESSUsage metrics
Keywords
Science & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer ScienceEnsemble learningfuzzy ARTMAPdeep learningmalware detectionparticle swarm optimizationbackpropagation algorithmFEATURE-SELECTION46 Information and Computing Sciences4602 Artificial Intelligence4604 Cybersecurity and Privacy4605 Data Management and Data Science4611 Machine LearningBioengineeringCognitive Sciences4602 Artificial intelligence4611 Machine learningArtificial Intelligence and Image Processing
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