论文标题
图形基于神经网络的Android恶意软件分类和跳跃知识
Graph Neural Network-based Android Malware Classification with Jumping Knowledge
论文作者
论文摘要
本文提出了一种基于图形神经网络（GNN）的新的Android恶意软件检测方法（JK）。 Android函数呼叫图（FCGS）由一组程序功能及其术间调用组成。因此，本文提出了一种基于GNN的方法，用于通过捕获有意义的心理内呼叫路径模式来检测Android恶意软件检测方法。此外，采用跳跃知识技术来最大程度地减少过度平滑问题的影响，这在GNN中很常见。该方法已使用两个基准数据集对所提出的方法进行了广泛的评估。与关键分类指标相比，与最先进的方法相比，结果表明了我们方法的优势，这证明了GNN在Android恶意软件检测和分类中的潜力。
This paper presents a new Android malware detection method based on Graph Neural Networks (GNNs) with Jumping-Knowledge (JK). Android function call graphs (FCGs) consist of a set of program functions and their inter-procedural calls. Thus, this paper proposes a GNN-based method for Android malware detection by capturing meaningful intra-procedural call path patterns. In addition, a Jumping-Knowledge technique is applied to minimize the effect of the over-smoothing problem, which is common in GNNs. The proposed method has been extensively evaluated using two benchmark datasets. The results demonstrate the superiority of our approach compared to state-of-the-art approaches in terms of key classification metrics, which demonstrates the potential of GNNs in Android malware detection and classification.
