Characterizing malicious Android apps by mining topic-specific data flow signatures
Information and Software Technology, ISSN: 0950-5849, Vol: 90, Page: 27-39
2017
- 36Citations
- 275Usage
- 52Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations36
- Citation Indexes36
- 36
- CrossRef2
- Usage275
- Downloads237
- Abstract Views38
- Captures52
- Readers52
- 52
Article Description
Context: State-of-the-art works on automated detection of Android malware have leveraged app descriptions to spot anomalies w.r.t the functionality implemented, or have used data flow information as a feature to discriminate malicious from benign apps. Although these works have yielded promising performance, we hypothesize that these performances can be improved by a better understanding of malicious behavior.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S095058491730366X; http://dx.doi.org/10.1016/j.infsof.2017.04.007; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85018397382&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S095058491730366X; https://api.elsevier.com/content/article/PII:S095058491730366X?httpAccept=text/xml; https://api.elsevier.com/content/article/PII:S095058491730366X?httpAccept=text/plain; https://ink.library.smu.edu.sg/sis_research/3675; https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=4677&context=sis_research; https://dx.doi.org/10.1016/j.infsof.2017.04.007
Elsevier BV
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