A Variant-Sensitive Malware Detection Method Based on Feature Contrast Enhancement

Malware poses a great threat to information security such as user data, privacy, and assets. Early detection before it has a real impact is the main countermeasure. However, the diversity of carriers and technologies has led to a huge gap between the training scenarios and actual scenarios of detection methods. This makes it difficult for supervision-based detection frameworks to identify new malware variants and complicates threat response. We propose a novel method that integrates frequency domain techniques with feature alignment to enhance variant malware detection, reducing distribution differences between labeled (source) and new (target) samples. By converting malware into grayscale images and applying discrete cosine transform (DCT) for improved feature extraction, followed by feature extraction via a deep residual network from both domains, our model systematically aligns features. This alignment is achieved through a tailored domain adaptation technique involving the minimization of classification and domain alignment losses, which ensures the consistent learning of features across varied domains. Such rigorous alignment not only enhances detection accuracy for both known and variant malware but also supports simultaneous detection across significant distribution differences. We conduct extensive experiments on two real-world datasets to evaluate the performance of various deep learning models under consistent and inconsistent domain distributions. Compared to existing methods, our approach improves accuracy by an average of 1.4% on the BIG2015 dataset, 3.2% on the MDA dataset, 2.75% on the Malimg dataset, and also achieves the best performance on the MaleVis dataset, with similar gains in precision, recall, and F1-score across all datasets.