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Data Mining for Business Applications pp 97–110Cite as

Security Data Mining: A Survey Introducing Tamper-Resistance

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Security data mining, a form of countermeasure, is the use of large-scale data analytics to dynamically detect a small number of adversaries who are constantly changing. It encompasses data-and results-related safeguards; and is relevant across multiple domains such as financial, insurance, and health. With reference to security data mining, there are specific and general problems, but the key solution and contribution of this chapter is still tamper-resistance. Tamper-resistance addresses most kinds of adversaries and makes it more difficult for an adversary to manipulate or circumvent security data mining; and consists of reliable data, anomaly detection algorithms, and privacy and confidentiality preserving results. In this way, organisations applying security data mining can better achieve accuracy for organisations, privacy for individuals in the data, and confidentiality between organisations which share the results.

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Authors and Affiliations

  1. A*STAR, Institute of Infocomm Research, Room 04-21 21, Heng Mui Keng Terrace, 119613, Singapore

    Clifton Phua & Mafruz Ashrafi

Authors
  1. Clifton Phua
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  2. Mafruz Ashrafi
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Correspondence to Clifton Phua .

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Editors and Affiliations

  1. School of Software Faculty of Engineering and Information Technology, University of Technology, PO Box 123, Sydney, Broadway, NSW 2007, Australia

    Longbing Cao  & Huaifeng Zhang  & 

  2. Department of Computer Science, University of Illinois at Chicago, 851 S. Morgan St., Chicago, IL, 60607

    Philip S. Yu

  3. Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology, University of Technology, PO Box 123, Sydney, Broadway, NSW 2007, Australia

    Chengqi Zhang

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© 2009 Springer Science+Business Media, LLC

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Phua, C., Ashrafi, M. (2009). Security Data Mining: A Survey Introducing Tamper-Resistance. In: Cao, L., Yu, P.S., Zhang, C., Zhang, H. (eds) Data Mining for Business Applications. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-79420-4_7

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  • DOI: https://doi.org/10.1007/978-0-387-79420-4_7

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-79419-8

  • Online ISBN: 978-0-387-79420-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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