Cybersecurity· 2026Q1
A dynamic malware detection method based on multi-modal features
- 0citations
- Q1SCImago
- 2026year
Short summary
A novel dynamic malware detection method fuses API sequences (in-band) and memory dump image features (out-of-band) using a multi-modal deep learning model, achieving 98.6% accuracy.
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Key points
- Proposes a dynamic malware detection method using multi-modal features.
- Utilizes API sequences (in-band) and memory dump image features (out-of-band).
- Employs a multi-modal deep learning model for feature fusion.
- Achieved 98.6% accuracy on a dataset from VirusTotal and CNET.
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract The rapid increase of sophisticated malware poses significant challenges to existing detection methods. To address the challenge, we propose a dynamic malware detection method based on multi-modal features. This approach utilizes two distinct modalities from in-band and out-of-band: the API (Application Programming Interface) sequence features obtained in sandbox, and the image features converted by memory dump files obtained out of VM (Virtual Machine). A multi-modal deep learning model is proposed to fuse these features to distinguish sophisticated malware. Experiments show that our method is effective, achieving 98.6% accuracy on a dataset collected from VirusTotal and CNET. It demonstrates significant improvements over single-modal methods using either text or image features alone, while combining the advantages of out-of-band and in-band dynamic detection, providing an effective solution for malware detection.
The authors' abstract, as published at the source. Cybersecurity, 2026 · DOI ↗
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Field: Signal Processing
Signal ProcessingComputer Science