Year: 2025
Type: article
Abstract: Raccoon Stealer malware is difficult to detect as it actively evades traditional methods. This study proposes Recealer, a hybrid detection model that integrates machine learning (ML) and deep learning (DL). The model applies static and dynamic analysis to extract features from sample files, which are first classified using an ML algorithm. Files with uncertain classification results are then transformed into grayscale images and analyzed by a convolutional neural network for improved precision. Experimental results demonstrate that Recealer achieves 94% overall detection accuracy, with the random forest algorithm attaining 97.53% in the ML stage and the DL stage reaching 95%. These findings indicate that Recealer is both efficient and reliable for detecting Raccoon Stealer malware.
Author: Aljahdali et al.
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $469
OpenAlex ID: W4414459801