Graph Neural Network-based Android Malware Classification with Jumping Knowledge
release_cubrowiqjbffhgzujdxa2452ue
by
Wai Weng Lo, Siamak Layeghy, Mohanad Sarhan, Marcus Gallagher, Marius Portmann
2022
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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