抄録
Mobile app stores, such as Google Play, play a vital role in the ecosystem of mobile device software distribution platforms. When users find an app of interest, they can acquire useful data from the app store to inform their decision regarding whether to install the app. 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. Therefore, miscreants can leverage such channels to conduct promotional attacks; for example, a miscreant may promote a malicious app by endowing it with a good reputation via fake ratings and reviews to encourage would-be victims to install the app. In this study, we have developed a system called PADetective that detects miscreants who are likely to be conducting promotional attacks. Using a 1723-entry labeled dataset, we demonstrate that the true positive rate of detection model is 90%, with a false positive rate of 5.8%. We then applied our system to an unlabeled dataset of 57M reviews written by 20M users for 1M apps to characterize the prevalence of threats in the wild. The PADetective system detected 289K reviewers as potential PA attackers. The detected potential PA attackers posted reviews to 136K apps, which included 21K malicious apps. We also report that our system can be used to identify potentially malicious apps that have not been detected by anti-virus checkers.
| 元の言語 | English |
|---|---|
| ページ（範囲） | 212-223 |
| ページ数 | 12 |
| ジャーナル | Journal of Information Processing |
| 巻 | 26 |
| DOI | |
| 出版物ステータス | Published - 2018 1 1 |
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ASJC Scopus subject areas
- Computer Science(all)
これを引用
PADetective : A systematic approach to automate detection of promotional attackers in mobile app store. / Sun, Bo; Luo, Xiapu; Akiyama, Mitsuaki; Watanabe, Takuya; Mori, Tatsuya.
：: Journal of Information Processing, 巻 26, 01.01.2018, p. 212-223.研究成果: Article
}
TY - JOUR
T1 - PADetective
T2 - A systematic approach to automate detection of promotional attackers in mobile app store
AU - Sun, Bo
AU - Luo, Xiapu
AU - Akiyama, Mitsuaki
AU - Watanabe, Takuya
AU - Mori, Tatsuya
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Mobile app stores, such as Google Play, play a vital role in the ecosystem of mobile device software distribution platforms. When users find an app of interest, they can acquire useful data from the app store to inform their decision regarding whether to install the app. 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. Therefore, miscreants can leverage such channels to conduct promotional attacks; for example, a miscreant may promote a malicious app by endowing it with a good reputation via fake ratings and reviews to encourage would-be victims to install the app. In this study, we have developed a system called PADetective that detects miscreants who are likely to be conducting promotional attacks. Using a 1723-entry labeled dataset, we demonstrate that the true positive rate of detection model is 90%, with a false positive rate of 5.8%. We then applied our system to an unlabeled dataset of 57M reviews written by 20M users for 1M apps to characterize the prevalence of threats in the wild. The PADetective system detected 289K reviewers as potential PA attackers. The detected potential PA attackers posted reviews to 136K apps, which included 21K malicious apps. We also report that our system can be used to identify potentially malicious apps that have not been detected by anti-virus checkers.
AB - Mobile app stores, such as Google Play, play a vital role in the ecosystem of mobile device software distribution platforms. When users find an app of interest, they can acquire useful data from the app store to inform their decision regarding whether to install the app. 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. Therefore, miscreants can leverage such channels to conduct promotional attacks; for example, a miscreant may promote a malicious app by endowing it with a good reputation via fake ratings and reviews to encourage would-be victims to install the app. In this study, we have developed a system called PADetective that detects miscreants who are likely to be conducting promotional attacks. Using a 1723-entry labeled dataset, we demonstrate that the true positive rate of detection model is 90%, with a false positive rate of 5.8%. We then applied our system to an unlabeled dataset of 57M reviews written by 20M users for 1M apps to characterize the prevalence of threats in the wild. The PADetective system detected 289K reviewers as potential PA attackers. The detected potential PA attackers posted reviews to 136K apps, which included 21K malicious apps. We also report that our system can be used to identify potentially malicious apps that have not been detected by anti-virus checkers.
KW - Machine learning
KW - Mobile app store
KW - Promotional attack
UR - http://www.scopus.com/inward/record.url?scp=85042108428&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85042108428&partnerID=8YFLogxK
U2 - 10.2197/ipsjjip.26.212
DO - 10.2197/ipsjjip.26.212
M3 - Article
AN - SCOPUS:85042108428
VL - 26
SP - 212
EP - 223
JO - Journal of Information Processing
JF - Journal of Information Processing
SN - 0387-5806
ER -