8. Security Challenges for the Critical Infrastructures of the Healthcare Sector

Maia, Eva; Praça, Isabel; Mantzana, Vasiliki; Gkotsis, Ilias; Petrucci, Paolo; Biasin, Elisabetta; Kamenjašević, Erik; Lammari, Nadira

doi:10.1561/9781680836875.ch8

Cited by 5 publications

(4 citation statements)

References 1 publication

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“…Bamiah et al [33] assert that CC adoption in healthcare minimizes complexity and enhances EHR management. Nevertheless, the healthcare sector's sensitivity to data breaches due to the nature of the information processed makes it a prime target for cyberattacks [34]. Research from the University of Minnesota's Public Health Department highlights a rise in cyberattacks against healthcare providers from 2016 to 2021, endangering the private health data of over 42 million patients [35].…”

Section: Cloud Computing Implementation In Healthcare: Strategies And...mentioning

confidence: 99%

Artificial Intelligence and Applications

2024

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Cloud computing (CC) represents a significant technological advancement in the UnitedStates (U.S.) healthcare. Despite its advantages like reduced costs, scalability, resource sharing, and high availability, CC raises concerns, especially in privacy and security. This study employs Grounded Theory methodology to delve into these concerns within cloudbased Healthcare Information Management Systems (HIMS) in the U.S., which operates under stringent patient privacy and security laws. The research focuses on healthcare organizations' strategies to mitigate these challenges. In-depth interviews and document analysis, conducted using a qualitative research strategy, will gather data from healthcare professionals and Information Technology (I.T.) specialists interacting with cloud-based HIMS. Through thematic analysis and constant comparison, the research will construct a theoretical framework showcasing CC's impact on HIMS privacy and security. This framework will establish a basis for subsequent research to improve U.S. healthcare delivery by directing organizations to adopt and implement cloud-based HIMS compliant with U.S. data privacy regulations.

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Section: Cloud Computing Implementation In Healthcare: Strategies And...mentioning

confidence: 99%

Artificial Intelligence and Applications

2024

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“…After its entry into force, the NIS Directive underwent a series of challenges in practice [ 23 , p. 147]. 11 The most crucial issue was its incoherent application due to the divergent Member State methodologies for identifying OES [ 17 ].…”

Section: Medical Device Cybersecurity In the Context Of The...mentioning

confidence: 99%

Cybersecurity of medical devices: new challenges arising from the AI Act and NIS 2 Directive proposals

Biasin

Kamenjašević

2022

Int. Cybersecur. Law Rev.

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Cyberattacks on the IT infrastructure of hospitals, electronic health records or medical devices that have taken place during the COVID-19 pandemic reaffirmed how crucial it is to ensure cybersecurity in the healthcare sector. Medical devices are regulated in the European Union (EU) through vertical product-specific legislation, such as the Medical Device Regulation (MDR), among others. The MDR foresees safety requirements implying cybersecurity obligations for medical device manufacturers. In 2021, the EU legislator put forward the Network and Information Security System Directive reform (NIS 2) and the Artificial Intelligence Act (AIA) proposal, containing additional cybersecurity requirements applicable to medical devices. This article analyses how the new reforms interact with the existing legislation from a cybersecurity perspective. The research finds that parallel provision of analogous cybersecurity requirements (especially on notification requirements) could lead to regulatory overlapping, fragmentation, and uneven levels of protection of individuals in the EU internal market. In the “Recommendations and conclusions”, the article provides policy recommendations to the EU legislator to help mitigate these risks.

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“…comprise another example in which physical tampering can significantly distort the system function (e.g., by changing antenna characteristics). According to the European Union Agency for Cybersecurity (ENISA) [5], physical tamper attacks within IoT applications are one of the main threats faced by healthcare organizations as well. An assumption in all these cases is that the transceivers must not be tampered for the system to work correctly.…”

Section: Introductionmentioning

confidence: 99%

DL-Based Physical Tamper Attack Detection in OFDM Systems with Multiple Receiver Antennas: A Performance–Complexity Trade-Off

Dehmollaian

Etzlinger

Torres

et al. 2022

Sensors

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This paper proposes two deep-learning (DL)-based approaches to a physical tamper attack detection problem in orthogonal frequency division multiplexing (OFDM) systems with multiple receiver antennas based on channel state information (CSI) estimates. The physical tamper attack is considered as the unwanted change of antenna orientation at the transmitter or receiver. Approaching the tamper attack scenario as a semi-supervised anomaly detection problem, the algorithms are trained solely based on tamper-attack-free measurements, while operating in general scenarios that may include physical tamper attacks. Two major challenges in the algorithm design are environmental changes, e.g., moving persons, that are not due to an attack and evaluating the trade-off between detection performance and complexity. Our experimental results from two different environments, comprising an office and a hall, show the proper detection performances of the proposed methods with different complexity levels. The optimal proposed method achieves a 93.32% true positive rate and a 10% false positive rate with a suitable level of complexity.

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8. Security Challenges for the Critical Infrastructures of the Healthcare Sector

Cited by 5 publications

References 1 publication

Artificial Intelligence and Applications

Artificial Intelligence and Applications

Cybersecurity of medical devices: new challenges arising from the AI Act and NIS 2 Directive proposals

DL-Based Physical Tamper Attack Detection in OFDM Systems with Multiple Receiver Antennas: A Performance–Complexity Trade-Off

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