Graph Neural Network-based Android Malware Classification with Jumping Knowledge

• URL: http://arxiv.org/abs/2201.07537v2
• Date: Thu, 20 Jan 2022 12:17:02 GMT
• Title: Graph Neural Network-based Android Malware Classification with Jumping Knowledge
• Authors: Wai Weng Lo, Siamak Layeghy, Mohanad Sarhan, Marcus Gallagher, Marius Portmann
• Abstract summary: This paper proposes a GNN-based method for Android malware detection by capturing meaningful intra-procedural call path patterns. A Jumping-Knowledge technique is applied to minimize the effect of the over-smoothing problem. The proposed method has been extensively evaluated using two benchmark datasets.
• Score: 3.408873763213743
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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