Abstract
Technology runs much of modern society’s daily functions due to how efficient, reliable, and easy it is to access and manage content anywhere at any time. This rapid growth has created an emphasis on cybersecurity to ensure data integrity in today’s digital realm and the future to come. Since more industries are relying on technology, cybersecurity is becoming more utilized as the foundation for success for many companies and individuals alike. However, as these new avenues for communication become part of daily life, cyber threats have also become more prevalent. One of these avenues affected includes healthcare telemedicine (Annaswarmy et al. 2020) which during COVID-19 pandemic provides patients with more convenient methods of medical services. To prevent cyber-attacks on these services through social engineering, among several defense techniques, including machine learning (ML), are being researched to mitigate the effects of human error. This paper provides recent social engineering attacks on healthcare systems, devices, and telemedicine services; and highlights the potential of machine learning in defending against social engineering attacks.
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Nguyen, C., Williams, W., Didlake, B., Mitchell, D., McGinnis, J., Dasgupta, D. (2022). Social Engineering Attacks in Healthcare Systems: A Survey. In: Choo, KK.R., Morris, T., Peterson, G., Imsand, E. (eds) National Cyber Summit (NCS) Research Track 2021. NCS 2021. Lecture Notes in Networks and Systems, vol 310. Springer, Cham. https://doi.org/10.1007/978-3-030-84614-5_11
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