Android Malware Detection Using One-Class Graph Neural Networks

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Deldar Fatemeh; ABADI MAHDI; Ebrahimifard Mohammad

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Journal Paper

Paper Information

Journal: THE ISC INTERNATIONAL JOURNAL OF INFORMATION SECURITY Year:2022 | Volume:14 | Issue:3 Page(s): 51-59

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Title

Android Malware Detection Using One-Class Graph Neural Networks

Author(s)

Deldar Fatemeh | ABADI MAHDI | Ebrahimifard Mohammad | Issue Writer Certificate

Keywords

Android Malware Detection

Attributed Function Call Graph

Graph Convolutional Layer

One-Class Classification

Semi-Supervised Deep Learning

Stacked Graph Autoencoder

Abstract

With the widespread use of Android smartphones, the Android platform has become an attractive target for cybersecurity attackers and malware authors. Meanwhile, the growing emergence of zero-day malware has long been a major concern for cybersecurity researchers. This is because malware that has not been seen before often exhibits new or unknown behaviors, and there is no documented defense against it. In recent years, deep learning has become the dominant machine learning technique for malware detection and could achieve outstanding achievements. Currently, most deep malware detection techniques are supervised in nature and require training on large datasets of benign and malicious samples. However, supervised techniques usually do not perform well against zero-day malware. Semi-supervised and unsupervised deep malware detection techniques have more potential to detect previously unseen malware. In this paper, we present MalGAE, a novel end-to-end deep malware detection technique that leverages one-class graph neural networks to detect Android malware in a semi-supervised manner. MalGAE represents each Android application with an Attributed Function Call Graph (AFCG) to benefit the ability of graphs to model complex relationships between data. It builds a deep one-class classifier by training a Stacked Graph Autoencoder with Graph Convolutional Layers on benign AFCGs. Experimental results show that MalGAE can achieve good detection performance in terms of different evaluation measures.

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APA: Copy

Deldar, Fatemeh, ABADI, MAHDI, & Ebrahimifard, Mohammad. (2022). Android Malware Detection Using One-Class Graph Neural Networks. THE ISC INTERNATIONAL JOURNAL OF INFORMATION SECURITY, 14(3), 51-59. SID. https://sid.ir/paper/1051818/en

Vancouver: Copy

Deldar Fatemeh, ABADI MAHDI, Ebrahimifard Mohammad. Android Malware Detection Using One-Class Graph Neural Networks. THE ISC INTERNATIONAL JOURNAL OF INFORMATION SECURITY[Internet]. 2022;14(3):51-59. Available from: https://sid.ir/paper/1051818/en

IEEE: Copy

Fatemeh Deldar, MAHDI ABADI, and Mohammad Ebrahimifard, “Android Malware Detection Using One-Class Graph Neural Networks,” THE ISC INTERNATIONAL JOURNAL OF INFORMATION SECURITY, vol. 14, no. 3, pp. 51–59, 2022, [Online]. Available: https://sid.ir/paper/1051818/en

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