dc.contributor.author | Liu, Xiaolei | |
dc.contributor.author | Du, Xiaojiang | |
dc.contributor.author | Zhang, Xiaosong | |
dc.contributor.author | Zhu, Qingxin | |
dc.contributor.author | Wang, Hao | |
dc.contributor.author | Guizani, Mohsen | |
dc.date.accessioned | 2019-03-15T12:19:26Z | |
dc.date.available | 2019-03-15T12:19:26Z | |
dc.date.created | 2019-03-13T11:54:26Z | |
dc.date.issued | 2019 | |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | http://hdl.handle.net/11250/2590249 | |
dc.description.abstract | any 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. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | MDPI | nb_NO |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Adversarial Samples on Android Malware Detection Systems for IoT Systems | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.description.version | publishedVersion | nb_NO |
dc.source.volume | 19 | nb_NO |
dc.source.journal | Sensors | nb_NO |
dc.source.issue | 4 | nb_NO |
dc.identifier.doi | 10.3390/s19040974 | |
dc.identifier.cristin | 1684430 | |
dc.description.localcode | © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | nb_NO |
cristin.unitcode | 194,63,10,0 | |
cristin.unitname | Institutt for datateknologi og informatikk | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |