Characterizing promotional attacks in mobile app store

Bo Sun, Xiapu Luo, Mitsuaki Akiyama, Takuya Watanabe, Tatsuya Mori

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

Mobile app stores, such as Google Play, play a vital role in the ecosystem of mobile apps. When users look for an app of interest, they can acquire useful data from the app store to facilitate their decision on installing the app or not. This data includes ratings, reviews, number of installs, and the category of the app. The ratings and reviews are the user-generated content (UGC) that affect the reputation of an app. Unfortunately, miscreants also exploit such channels to conduct promotional attacks (PAs) that lure victims to install malicious apps. In this paper, we propose and develop a new system called PADetective to detect miscreants who are likely to be conducting promotional attacks. Using a dataset with 1,723 of labeled samples, we demonstrate that the true positive rate of detection model is 90%, with a false positive rate of 5.8%. We then applied PADetective to a large dataset for characterizing the prevalence of PAs in the wild and find 289 K potential PA attackers who posted reviews to 21 K malicious apps.

本文言語English
ホスト出版物のタイトルApplications and Techniques in Information Security - 8th International Conference, ATIS 2017, Proceedings
編集者Dong Seong Kim, Gang Li, Xuyun Zhang, Lynn Batten
出版社Springer Verlag
ページ113-127
ページ数15
ISBN(印刷版)9789811054204
DOI
出版ステータスPublished - 2017
イベント8th International Conference on Applications and Techniques in Information Security, ATIS 2017 - Auckland, New Zealand
継続期間: 2017 7 62017 7 7

出版物シリーズ

名前Communications in Computer and Information Science
719
ISSN(印刷版)1865-0929

Other

Other8th International Conference on Applications and Techniques in Information Security, ATIS 2017
CountryNew Zealand
CityAuckland
Period17/7/617/7/7

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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