Active Learning-Based Mobile Malware Detection Utilizing Auto-Labeling and Data Drift Detection

Zhe Deng, Arthur Hubert, Sadok Ben Yahia, Hayretdin Bahsi

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

Abstract

Machine learning-based detection methods have demonstrated high performance in mobile malware detection. However, changes in mobile malware over time induce a sig-nificant challenge for malware detection systems running in operational environments. The development of non-stationary models to address concept drift in the threat landscape has garnered significant research interest. Auto-labeling is using automated algorithms to assign labels to data without manual intervention. This study explores the method for introducing auto-labeling in active learning with data drift detection. It achieves satisfying results for the lowest possible cost while succeeding in adapting to changes in the data for a long period.

Original languageEnglish (US)
Title of host publicationProceedings of the 2024 IEEE International Conference on Cyber Security and Resilience, CSR 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages146-151
Number of pages6
ISBN (Electronic)9798350375367
DOIs
StatePublished - 2024
Externally publishedYes
Event2024 IEEE International Conference on Cyber Security and Resilience, CSR 2024 - Hybrid, London, United Kingdom
Duration: Sep 2 2024Sep 4 2024

Publication series

NameProceedings of the 2024 IEEE International Conference on Cyber Security and Resilience, CSR 2024

Conference

Conference2024 IEEE International Conference on Cyber Security and Resilience, CSR 2024
Country/TerritoryUnited Kingdom
CityHybrid, London
Period9/2/249/4/24

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Safety, Risk, Reliability and Quality

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