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https://doi.org/10.1109/scam.2...
Article . 2017 . Peer-reviewed
Data sources: Crossref
https://dx.doi.org/10.48550/ar...
Article . 2020
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
DBLP
Conference object . 2023
Data sources: DBLP
DBLP
Article . 2020
Data sources: DBLP
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Security Smells in Android

Authors: Mohammad Ghafari; Pascal Gadient; Oscar Nierstrasz;

Security Smells in Android

Abstract

The ubiquity of smartphones, and their very broad capabilities and usage, make the security of these devices tremendously important. Unfortunately, despite all progress in security and privacy mechanisms, vulnerabilities continue to proliferate. Research has shown that many vulnerabilities are due to insecure programming practices. However, each study has often dealt with a specific issue, making the results less actionable for practitioners. To promote secure programming practices, we have reviewed related research, and identified avoidable vulnerabilities in Android-run devices and the "security code smells" that indicate their presence. In particular, we explain the vulnerabilities, their corresponding smells, and we discuss how they could be eliminated or mitigated during development. Moreover, we develop a lightweight static analysis tool and discuss the extent to which it successfully detects several vulnerabilities in about 46,000 apps hosted by the official Android market.

2017 IEEE 17th International Working Conference on Source Code Analysis and Manipulation (SCAM)

Related Organizations
Keywords

Software Engineering (cs.SE), FOS: Computer and information sciences, Computer Science - Software Engineering, Computer Science - Cryptography and Security, Cryptography and Security (cs.CR)

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
38
Top 10%
Top 10%
Top 10%
Green