Show simple item record

dc.creatorLiu, X
dc.creatorDu, X
dc.creatorZhang, X
dc.creatorZhu, Q
dc.creatorWang, H
dc.creatorGuizani, M
dc.date.accessioned2020-12-11T20:05:50Z
dc.date.available2020-12-11T20:05:50Z
dc.date.issued2019-02-02
dc.identifier.issn1424-8220
dc.identifier.issn1424-8220
dc.identifier.doihttp://dx.doi.org/10.34944/dspace/4326
dc.identifier.other30823597 (pubmed)
dc.identifier.urihttp://hdl.handle.net/20.500.12613/4344
dc.description.abstract© 2019 by the authors. Licensee MDPI, Basel, Switzerland. Many IoT (Internet of Things) systems run Android systems or Android-like systems. With the continuous development of machine learning algorithms, the learning-based Android malware detection system for IoT devices has gradually increased. However, these learning-based detection models are often vulnerable to adversarial samples. An automated testing framework is needed to help these learning-based malware detection systems for IoT devices perform security analysis. The current methods of generating adversarial samples mostly require training parameters of models and most of the methods are aimed at image data. To solve this problem, we propose a testing framework for learning-based Android malware detection systems (TLAMD) for IoT Devices. The key challenge is how to construct a suitable fitness function to generate an effective adversarial sample without affecting the features of the application. By introducing genetic algorithms and some technical improvements, our test framework can generate adversarial samples for the IoT Android application with a success rate of nearly 100% and can perform black-box testing on the system.
dc.format.extent974-974
dc.language.isoen
dc.relation.haspartSensors (Switzerland)
dc.relation.isreferencedbyMDPI AG
dc.rightsCC BY
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectInternet of Things
dc.subjectmalware detection
dc.subjectadversarial samples
dc.subjectmachine learning
dc.titleAdversarial samples on android malware detection systems for IoT systems
dc.typeArticle
dc.type.genreJournal Article
dc.relation.doi10.3390/s19040974
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.creator.orcidDu, Xiaojiang|0000-0003-4235-9671
dc.date.updated2020-12-11T20:05:47Z
refterms.dateFOA2020-12-11T20:05:51Z


Files in this item

Thumbnail
Name:
Adversarial Samples on Android ...
Size:
731.3Kb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record

CC BY
Except where otherwise noted, this item's license is described as CC BY