.png){kind=icon}
.png){kind=icon}
Return
Adaptive malware detection using sequential feature selection: A dueling double deep Q-Network framework for intelligent classification
.png){kind=icon}
OA
AI
DOI：10.1016/j.jisa.2026.104407.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• Formulates malware classification as a Markov Decision Process with episodic feature acquisition, achieving superior performance across diverse datasets: 99.20% F1-score on Microsoft Big2015, 98.64% on BODMAS, and 85.07% on EMBER 2018 using reinforcement learning. • Demonstrates systematic superiority over traditional approaches through comprehensive ablation studies, where static feature selection methods exhibit severe performance degradation (up to 10.40% F1-score reduction) while D3QN maintains consistent improvements across all evaluation scenarios. • Validates robust transferability with 76.08% average recall on unseen EMBER 2024 malware variants across six diverse file formats, demonstrating 27.55% relative improvement over traditional methods and effective zero-day threat detection capabilities. • Introduces quantitative intelligence assessment framework proving strategic learning behavior with 62.5% categorical preference deviation from random baselines, 57.7% feature specialization, and autonomous discovery of domain-aligned cybersecurity patterns without explicit supervision.
Keywords:
Malware classification
Machine learning
Reinforcement learning
Sequential feature selection
Dueling double deep Q-Network
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.7
Papers:
1.9K
Citations:
4.9K