Adversarial attacks on mobile malware detection

Maryam Shahpasand, Leonard Hamey, Dinusha Vatsalan, Minhui Xue

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

14 Citations (Scopus)


In recent years, machine learning approaches have been widely adopted for computer security tasks, including malware detection. Malware is a potent threat and an ongoing issue especially on smartphones which account for more than half of global web traffic. Although detection solutions are improving with the advances in machine learning techniques, they have been shown to be vulnerable to adversarial samples that carefully crafted perturbation enables them to evade detection. We propose a machine learning based model to attack malware classifiers leveraging the expressive capability of generative adversarial networks (GANs). We use GANs to generate effective adversarial samples by implying a threshold on the distortion amount on the generated samples. We show that the generated samples can bypass detection in 99% of attempts using a real Android application dataset.
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 PublicationPiscataway NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages4
ISBN (Electronic)9781728118116
ISBN (Print)9781728118123
Publication statusPublished - 21 Mar 2019
Event2019 IEEE 1st International Workshop on Artificial Intelligence for Mobile, AI4Mobile '19 - Hangzhou, China
Duration: 24 Feb 201924 Feb 2019


Conference2019 IEEE 1st International Workshop on Artificial Intelligence for Mobile, AI4Mobile '19


  • Malware
  • Machine learning
  • Adversarial Machine Learning


Dive into the research topics of 'Adversarial attacks on mobile malware detection'. Together they form a unique fingerprint.

Cite this