基于BERT Transformer的基于图的Android恶意软件检测与分类

A. Saracino, Marco Simoni
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引用次数: 0

摘要

在本文中,我们提出了一种新的Android恶意软件分析和分类方法,该方法利用BERT(来自变压器的双向编码器表示)的功能对Android API调用图生成的API调用序列进行分类。通过利用API调用图,我们的方法捕获了API调用之间复杂的关系和依赖关系,从而能够更深入地了解Android恶意软件所表现出的行为。我们的研究结果表明,我们的方法在将API调用序列分类为恶意或良性方面达到了很高的准确性,并且该方法也为分类Android恶意软件提供了一个有前途的解决方案,可以帮助减轻恶意Android应用程序带来的风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Graph-Based Android Malware Detection and Categorization through BERT Transformer
In this paper, we propose a novel approach to Android malware analysis and categorization that leverages the power of BERT (Bidirectional Encoder Representations from Transformers) to classify API call sequences generated from Android API Call Graph. By utilizing the API Call Graph, our approach captures the intricate relationships and dependencies between API calls, enabling a deeper understanding of the behavior exhibited by Android malware. Our results show that our approach achieves high accuracy in classifying API call sequences as malicious or benign and the method provides a promising solution also for categorizing Android malware and can help mitigate the risks posed by malicious Android applications.
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