In recent years we have witnessed an increase in cyber threats and malicious software attacks on different platforms with important consequences to persons and businesses. It has become critical to find automated machine learning techniques to proactively defend against malware. Transformers, a category of attention-based deep learning techniques, have recently shown impressive results in solving different tasks mainly related to the field of Natural Language Processing (NLP). In this paper, we propose the use of a Transformers' architecture to automatically detect malicious software. We propose a model based on BERT (Bidirectional Encoder Representations from Transformers) which performs a static analysis on the source code of Android applications using preprocessed features to characterize existing malware and classify it into different representative malware categories. The obtained results are promising and show the high performance obtained by Transformer-based models for malicious software detection.
翻译:近年来,我们看到对不同平台的网络威胁和恶意软件袭击增加,给个人和企业带来重要后果,发现自动机器学习技术以主动防范恶意软件已经变得至关重要。 以注意力为基础的深层学习技术类别变换器最近在解决主要与自然语言处理领域相关的不同任务方面取得了令人印象深刻的成果。 在本文中,我们提议使用变换器结构自动检测恶意软件。我们提议了一个基于BERT的模型(来自变换器的双向编码表示器),该模型利用预处理的特性来描述现有恶意软件并将其分类为不同的有代表性的恶意软件类别,对 Android应用源代码进行静态分析。获得的结果很有希望,并展示了以变换器为基础的模型在恶意软件检测方面的高性能。