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False Data Injection Attacks in Healthcare

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Data Mining (AusDM 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 845))

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Abstract

False data injection attacks (FDIA) are widely studied mainly in the area of smart grid, power systems and wireless sensor networks. In this paper, an overview of the FDIA is proposed including the definition and detection techniques proposed so far. The main focus of this paper is to create awareness about the impact of the FDIA in domains other than smart grid such as healthcare. The impact of FDIA in healthcare is overlooked for last couple of years around the globe. However, the recent information security incidents rise in the healthcare sector reaffirms the requirements of preventive measures for FDIA in healthcare. In this paper, we also focus on the emerging attacks on the healthcare domain to understand the importance of FDIA prevention techniques.

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Authors and Affiliations

  1. Canberra Institute of Technology, Reid, ACT, 2601, Australia

    Mohiuddin Ahmed & Abu S. S. M. Barkat Ullah

Authors
  1. Mohiuddin Ahmed
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  2. Abu S. S. M. Barkat Ullah
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Corresponding author

Correspondence to Mohiuddin Ahmed .

Editor information

Editors and Affiliations

  1. RMIT University, Melbourne, Victoria, Australia

    Yee Ling Boo

  2. University of Wollongong, Wollongong, New South Wales, Australia

    David Stirling

  3. La Trobe University, Melbourne, Victoria, Australia

    Lianhua Chi

  4. School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, South Australia, Australia

    Lin Liu

  5. La Trobe University, Melbourne, Victoria, Australia

    Kok-Leong Ong

  6. Microsoft Pty Ltd, Singapore, Singapore

    Graham Williams

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Ahmed, M., Barkat Ullah, A.S.S.M. (2018). False Data Injection Attacks in Healthcare. In: Boo, Y., Stirling, D., Chi, L., Liu, L., Ong, KL., Williams, G. (eds) Data Mining. AusDM 2017. Communications in Computer and Information Science, vol 845. Springer, Singapore. https://doi.org/10.1007/978-981-13-0292-3_12

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  • DOI: https://doi.org/10.1007/978-981-13-0292-3_12

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-0291-6

  • Online ISBN: 978-981-13-0292-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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