Detecting and categorizing Android malware with graph neural networks

Peng Xu, Claudia Eckert, Apostolis Zarras

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

Android is the most dominant operating system in the mobile ecosystem. As expected, this trend did not go unnoticed by miscreants, and quickly enough, it became their favorite platform for discovering new victims through malicious apps. These apps have become so sophisticated that they can bypass anti-malware measures implemented to protect the users. Therefore, it is safe to admit that traditional anti-malware techniques have become cumbersome, sparking the urge to come up with an efficient way to detect Android malware. In this paper, we present a novel Natural Language Processing (NLP) inspired Android malware detection and categorization technique based on Function Call Graph Embedding. We design a graph neural network (graph embedding) based approach to convert the whole graph structure of an Android app to a vector. We then utilize the graphs' vectors to detect and categorize the malware families. Our results reveal that graph embedding yields better results as we get 99.6% accuracy on average for the malware detection and 98.7% accuracy for the malware categorization.

Original languageEnglish
Title of host publicationProceedings of the 36th Annual ACM Symposium on Applied Computing, SAC 2021
PublisherAssociation for Computing Machinery (ACM)
Pages409-412
Number of pages4
ISBN (Electronic)9781450381048
DOIs
Publication statusPublished - 2021
Event36th Annual ACM Symposium on Applied Computing, SAC 2021 - Virtual, Online, Korea, Republic of
Duration: 22 Mar 202126 Mar 2021

Publication series

NameProceedings of the ACM Symposium on Applied Computing

Conference

Conference36th Annual ACM Symposium on Applied Computing, SAC 2021
CountryKorea, Republic of
CityVirtual, Online
Period22/03/2126/03/21

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