Encyclopedia of Social Network Analysis and Mining

Living Edition
| Editors: Reda Alhajj, Jon Rokne

Android Malware Pattern Recognition for Fraud Detection and Attribution: A Case Study

  • Sergio de los SantosEmail author
  • Antonio Guzmán
  • Carmen Torrano
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-7163-9_110173-1



Antivirus signatures

Singularity used by antivirus engines to identify certain piece of malware.


Black Hat App Store Optimization. It refers to any technique used to artificially rise up the number of downloads and rating of applications in markets.


Greenwich mean time.


Pre-hypertext processor.


Code written to cause harm to the device where it runs or, indirectly, the person using the device.


Machine learning is concerned with how to construct computer programs that automatically learn with experience. Machine learning systems are based on the establishment of an explicit or implicit model that allows to categorize the analyzed patterns (Tsai et al. 2009).

This automation capability is very useful in the cyberspace, where big amounts of data need to be handled. In regard to that, the term Big Data is frequently used. Big Data usually includes datasets with sizes...

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  1. Chiang B, Technode (2012) Shuabang: the great evil in China. In: Business insider. Available via DIALOG. http://www.businessinsider.com/shuabang-the-great-evil-in-china-2012-11. Accessed 9 May 2016
  2. De los Santos S, García MA (2015) Detected some “clickers” in Google Play simulating apps and games. In: Eleven Paths Blog. Available via DIALOG. https://securelist.com/analysis/kaspersky-security-bulletin/73839/mobile-malware-evolution-2015/. Accessed 9 May 2016
  3. De Mauro A, Greco M, Grimaldi M (2016) A formal definition of big data based on its essential features. Libr Rev 65:122135. doi: 10.1108/LR-06-2015-0061 CrossRefGoogle Scholar
  4. Laney D (2001) 3D data management: controlling data volume, velocity and variety. In: Gartner. Available via DIALOG. https://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf. Accessed 14 July 2016
  5. Pettey C, Goasduff L (2011) Gartner says solving ‘big data’ challenge involves more than just managing volumes of data. In: Gartner. Available via DIALOG. http://www.gartner.com/newsroom/id/1731916. Accessed 14 July 2016
  6. Širmer J (2015) Fobus, the sneaky little thief that could. In: Avast blog. Available via DIALOG. https://blog.avast.com/2015/01/15/fobus-the-sneaky-little-thief-that-could/. Accessed 9 May 2016
  7. Tsai CF, Hsu YF, Lin CY, Lin WY (2009) Intrusion detection by machine learning: a review. Expert Syst Appl 36(10):11994–12000CrossRefGoogle Scholar
  8. University Alliance (2016) What is big data? In: Villanova University web page. Available via DIALOG. http://www.villanovau.com/resources/bi/what-is-big-data/#.V4die6KaJuI. Accessed 14 July 2016
  9. Unuchek R, Chebyshev V (2015) Mobile malware evolution 2015. In: Securelist, Kaspersky Security Bulletin. Available via DIALOG. https://securelist.com/analysis/kaspersky-security-bulletin/73839/mobile-malware-evolution-2015/. Accessed 9 May 2016

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  • Sergio de los Santos
    • 1
    Email author
  • Antonio Guzmán
    • 1
  • Carmen Torrano
    • 1
  1. 1.ElevenPathsMalagaSpain

Section editors and affiliations

  • Rosa M. Benito
    • 1
  • Juan Carlos Losada
    • 2
  1. 1.Universidad Politécnica de MadridMadridSpain
  2. 2.Universidad Politécnica de MadridMadridSpain