False data injection attacks in healthcare

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

Research output: A Conference proceeding or a Chapter in BookConference contribution

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationData Mining - 15th Australasian Conference, AusDM 2017, Revised Selected Papers
EditorsDavid Stirling, Yee Ling Boo, Lianhua Chi, Kok-Leong Ong, Lin Liu, Graham Williams
Place of PublicationSingapore
PublisherSpringer
Pages192-197
Number of pages6
ISBN (Electronic)9789811302923
ISBN (Print)9789811302916
DOIs
Publication statusPublished - 14 Apr 2018
Externally publishedYes
Event15th Australasian Conference on Data Mining, AusDM 2017 - Melbourne, Australia
Duration: 19 Aug 201720 Aug 2017

Publication series

NameCommunications in Computer and Information Science
Volume845
ISSN (Print)1865-0929

Conference

Conference15th Australasian Conference on Data Mining, AusDM 2017
CountryAustralia
CityMelbourne
Period19/08/1720/08/17

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Ahmed, M., & Barkat Ullah, A. S. S. M. (2018). False data injection attacks in healthcare. In D. Stirling, Y. L. Boo, L. Chi, K-L. Ong, L. Liu, & G. Williams (Eds.), Data Mining - 15th Australasian Conference, AusDM 2017, Revised Selected Papers (pp. 192-197). (Communications in Computer and Information Science; Vol. 845). Singapore: Springer. https://doi.org/10.1007/978-981-13-0292-3_12