Mining Patterns of Sensitive Data Usage.
Saved in:
| Title: | Mining Patterns of Sensitive Data Usage. |
|---|---|
| Authors: | Avdiienko, Vitalii1 avdiienko@cs.uni-saarland.de |
| Source: | ICSE: International Conference on Software Engineering. 2015, p891-894. 4p. |
| Subjects: | Application software research, Android (Operating system), Mobile apps, Data mining, Data plans |
| Abstract: | When a user downloads an Android application from a market, she does not know much about its actual behavior. A brief description, a set of screenshots, and the list of permissions, which give a high level intuition of what the application might be doing, are all the user sees before installing and running the application on his device. These elements are not enough to decide whether the application is secure, and for sure they do not indicate whether it might violate the user's privacy by leaking some sensitive data. The goal of my thesis is to employ both static and dynamic taint analyses to gather information on how Android applications use sensitive data. The main hypothesis of this work is that malicious and benign mobile applications differ in how they use sensitive data, and consequently information flow can be used effectively to identify malware. [ABSTRACT FROM AUTHOR] |
| Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 111750070 AccessLevel: 6 PubType: Conference PubTypeId: conference PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Mining Patterns of Sensitive Data Usage. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Avdiienko%2C+Vitalii%22">Avdiienko, Vitalii</searchLink><relatesTo>1</relatesTo><i> avdiienko@cs.uni-saarland.de</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22ICSE%3A+International+Conference+on+Software+Engineering%22">ICSE: International Conference on Software Engineering</searchLink>. 2015, p891-894. 4p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Application+software+research%22">Application software research</searchLink><br /><searchLink fieldCode="DE" term="%22Android+%28Operating+system%29%22">Android (Operating system)</searchLink><br /><searchLink fieldCode="DE" term="%22Mobile+apps%22">Mobile apps</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Data+plans%22">Data plans</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: When a user downloads an Android application from a market, she does not know much about its actual behavior. A brief description, a set of screenshots, and the list of permissions, which give a high level intuition of what the application might be doing, are all the user sees before installing and running the application on his device. These elements are not enough to decide whether the application is secure, and for sure they do not indicate whether it might violate the user's privacy by leaking some sensitive data. The goal of my thesis is to employ both static and dynamic taint analyses to gather information on how Android applications use sensitive data. The main hypothesis of this work is that malicious and benign mobile applications differ in how they use sensitive data, and consequently information flow can be used effectively to identify malware. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=111750070 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/ICSE.2015.285 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 4 StartPage: 891 Subjects: – SubjectFull: Application software research Type: general – SubjectFull: Android (Operating system) Type: general – SubjectFull: Mobile apps Type: general – SubjectFull: Data mining Type: general – SubjectFull: Data plans Type: general Titles: – TitleFull: Mining Patterns of Sensitive Data Usage. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Avdiienko, Vitalii IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2015 Type: published Y: 2015 Titles: – TitleFull: ICSE: International Conference on Software Engineering Type: main |
| ResultId | 1 |
