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GNN-based Android Malware Detection with Jumping Knowledge. (arXiv:2201.07537v1 [cs.CR])
Jan. 20, 2022, 2:10 a.m. | Wai Weng Lo, Siamak Layeghy, Mohanad Sarhan, Marcus Gallagher, Marius Portmann
cs.LG updates on arXiv.org arxiv.org
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 …
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