| Autor: |
Rajib Bag, Manab Kumar Das, Atanu Das, Joy Chatterjee, Sayon Ghosh |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
Studies in Big Data ISBN: 9789811541117 |
| DOI: |
10.1007/978-981-15-4112-4_12 |
| Popis: |
Internet of things (IoT) is one of the most optimistic technologies that have remarkably changed the concept of the healthcare industry which offers a huge added value in the identification of diseases and monitoring the patient remotely. The research community and the public sector are very much focused on this application domain to develop various e-health regulations and policies. However, IoT-based healthcare systems suffer from several security issues that are varied from other domains in terms of methodologies, motivations, and consequences, due to the complexity of the environment and the nature of the deployed devices. The expansion of healthcare IoT devices, along with the absence of network segmentation, inadequate access controls, and dependency on legacy systems has widen attack area for cybercriminals to exploit or steal personally identifiable information (PII) and protected health information (PHI) without interrupting healthcare information transmission processes. Predicting attacks quantitatively may reduce the risk of fraudulent data; different approaches were noticed to identify and predict the IoT intrusions such as network metric based and machine learning approach. This work will review the related security models to identify the approaches of intrusion detection and prediction related to IoT devices as well as software connected in healthcare systems. This provides an overview of the most recent threats and security issues for IoT-based healthcare systems that may affect the efficient and effective functioning of such infrastructures. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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