How can we craft large-scale Android malware?

An automated poisoning attack

Sen Chen, Minhui Xue, Lingling Fan, Lei Ma, Yang Liu, Lihua Xu

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

3 Citations (Scopus)

Abstract

Android malware, is one of the most serious threats to mobile security. Today, machine learning-based approach is one of the most promising approaches in detecting Android malware. However, our previous experiments show that sophisticated attackers can craft large-scale Android malware to pollute training data and pose an automated poisoning attack on machine learning-based malware detection systems (e.g., Drebin, Droidapiminer, Stormdroid, and Mamadroid), and eventually mislead the detection tools. We further examine how machine learning classifiers can be mislead under four different attack models and significantly reduce detection accuracy. Apart from Android malware, to better protect mobile devices, we also discuss a general threat model of Android devices to investigate the capabilities of different attackers.

Original languageEnglish
Title of host publicationAI4Mobile 2019 - 2019 IEEE 1st International Workshop on Artificial Intelligence for Mobile
EditorsYang Liu, Lei Ma, Li Li, Minhui Xue
Place of Publication Piscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages21-24
Number of pages4
ISBN (Electronic)9781728118116
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event1st IEEE International Workshop on Artificial Intelligence for Mobile, AI4Mobile 2019 - Hangzhou, China
Duration: 24 Feb 2019 → …

Publication series

NameAI4Mobile 2019 - 2019 IEEE 1st International Workshop on Artificial Intelligence for Mobile

Conference

Conference1st IEEE International Workshop on Artificial Intelligence for Mobile, AI4Mobile 2019
CountryChina
CityHangzhou
Period24/02/19 → …

Keywords

  • Adversarial machine learning
  • Android malware detection
  • Poisoning attack

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