A novel dataset for fake android anti-malware detection

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBNpeer-review

Abstract

Today in the world people are able to get all types of Android applications (apps) from the app store or various sources over the Internet. A large number of apps is being produced daily, some of which are infected with malware. Thus, the use of anti-malware identification tools is essential. At the same time, a number of attackers who exploit a number of anti-malwares have been doing obtaining information from mobile phones in various ways, such as decompiling or infecting anti-malware. Therefore, in this paper, we developed a classification dataset from collected anti-malware data looking for fraudulent anti-malware products. Additionally, we applied various machine learning algorithms and we propose a combination of algorithms which provides high accuracy over various evaluation tests, showing that our approach is both practical and effective.

Original languageEnglish
Title of host publication10th International Conference on Web Intelligence, Mining and Semantics
Place of PublicationNew York
PublisherACM
Pages205-209
Number of pages5
ISBN (Print)9781450375429
DOIs
Publication statusPublished - 30 Jun 2020
Event10th International Conference on Web Intelligence, Mining and Semantics - Biarritz, France
Duration: 30 Jun 20203 Jul 2020
https://wims2020.sigappfr.org/

Conference

Conference10th International Conference on Web Intelligence, Mining and Semantics
Abbreviated titleWIMS 2020
CountryFrance
CityBiarritz
Period30/06/203/07/20
Internet address

Bibliographical note

© 2020 Copyright is held by the owner/author(s). This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in WIMS 2020: Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics, http://dx.doi.org/10.1145/3405962.3405980

Keywords

  • Android
  • Anti-malware
  • Cyber security
  • Fake anti-malware detection
  • Machine learning
  • Malware

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