Poster: Towards Sustainable Android Malware Detection

Approaches to Android malware detection built on supervised learning are commonly subject to frequent retraining, or the trained classifier may fail to detect newly emerged or emerging kinds of malware. This work targets a sustainable Android malware detector that, once trained on a dataset, can con...

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Bibliographic Details
Published in2018 IEEE/ACM 40th International Conference on Software Engineering: Companion (ICSE-Companion) pp. 350 - 351
Main Authors Cai, Haipeng, Jenkins, John
Format Conference Proceeding
LanguageEnglish
Published ACM 01.05.2018
Subjects
Android
Androids
Detection
Detectors
Feature extraction
Humanoid robots
Malware
Measurement
Sustainability
Training
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Summary:Approaches to Android malware detection built on supervised learning are commonly subject to frequent retraining, or the trained classifier may fail to detect newly emerged or emerging kinds of malware. This work targets a sustainable Android malware detector that, once trained on a dataset, can continue to effectively detect new malware without retraining. To that end, we investigate how the behaviors of benign and malicious apps evolve over time, and identify the most consistently discriminating behavioral traits of benign apps from malware. Our preliminary results reveal a promising prospect of this approach. On a benchmark set across seven years, our approach achieved highly competitive detection accuracy that sustained up to five years, outperforming the state of the art which sustained up to two years.
ISSN:2574-1934
DOI:10.1145/3183440.3195004