API ve Görüntü Özelliklerinin Birleştirilmesiyle Dinamik Kötü Amaçlı Yazılım Tespiti

Özet (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.

Yazarların özeti; kaynağından alınmıştır. Cybersecurity, 2026 · DOI ↗