Spectral-Based Directed Graph Network for Malware Detection

Abstract

As a kind of behavioral-feature based malware detection approach, spectral graph-based deep learning has attracted considerable research efforts with the fast growth of threats of malicious programs. However, previous spectral based graph neural networks can hardly be applied to directed graphs due to the asymmetrical nature of the graph adjacency matrix. In order to address the issues of existing techniques, we propose a Spectral-based Directed Graph Network (SDGNet) architecture to classify directed graphs. In SDGNet, the weighted graph matrix normalization methods transform the graph adjacency matrix into three symmetrical graph matrices that describe different aspects of node information interaction. Then, the SDGNet extracts graph representations with different layers of multi-aspect directed GCN. On each layer, three node embeddings learned from the symmetrical graph matrices are fused together for a graph representation. The multi-layer graph representations are further concatenated together to form a comprehensive representation for classification with a combined loss function. We evaluate the proposed algorithm on a public benchmark data, and the experimental results show that it outperforms state-of-the-art algorithms.