Enhancing Internet of Medical Things security with artificial intelligence: A comprehensive review

Messinis, Sotirios; Temenos, Nikos; Protonotarios, Nicholas E.; Rallis, Ioannis; Kalogeras, Dimitrios; Doulamis, Nikolaos

doi:10.1016/j.compbiomed.2024.108036

Cited by 11 publications

(2 citation statements)

References 129 publications

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“…Mitre ATT&CK consists of fourteen phases [44]: The Mitre ATT&CK framework can be applied to broad kinds of targets, including, financial systems [45], healthcare [46] and Industrial Control Systems (ICS) [47].…”

Section: Mitre Attandck Frameworkmentioning

confidence: 99%

Bridging the Gap: A Survey and Classification of Research-Informed Ethical Hacking Tools

Modesti,

Golightly,

Holmes

et al. 2024

JCP

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The majority of Ethical Hacking (EH) tools utilised in penetration testing are developed by practitioners within the industry or underground communities. Similarly, academic researchers have also contributed to developing security tools. However, there appears to be limited awareness among practitioners of academic contributions in this domain, creating a significant gap between industry and academia’s contributions to EH tools. This research paper aims to survey the current state of EH academic research, primarily focusing on research-informed security tools. We categorise these tools into process-based frameworks (such as PTES and Mitre ATT&CK) and knowledge-based frameworks (such as CyBOK and ACM CCS). This classification provides a comprehensive overview of novel, research-informed tools, considering their functionality and application areas. The analysis covers licensing, release dates, source code availability, development activity, and peer review status, providing valuable insights into the current state of research in this field.

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Section: Mitre Attandck Frameworkmentioning

confidence: 99%

Bridging the Gap: A Survey and Classification of Research-Informed Ethical Hacking Tools

Modesti,

Golightly,

Holmes

et al. 2024

JCP

View full text Add to dashboard Cite

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“…A combination of human knowledge and computer derived assistance could be used to accomplish this. Various fields have already proven that ML is a fast and precise means of obtaining valid results; therefore, physicians can benefit from employing this intelligent technology to make better decisions [4,5]. Doctors and hospitals can regulate this immense amount of data by applying ML techniques, keeping electronic health records of patients accessible from anywhere at any time, and thus being able to provide better treatment.…”

Section: Introductionmentioning

confidence: 99%

The Role of Machine Learning in Managing and Organizing Healthcare Records

Alghamdi,

Al-Khasawneh,

Alarood

et al. 2024

Eng. Technol. Appl. Sci. Res.

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With the exponential growth of medical data, Machine Learning (ML) algorithms are becoming increasingly important to the management and organization of healthcare information. This study aims to explore the role that ML can play in optimizing the management and organization of healthcare records, by identifying the challenges, advantages, and limitations associated with this technology. Consequently, the current study will contribute to the understanding of how ML might be applied to the healthcare industry in a variety of circumstances. Using the findings of this study, healthcare professionals, researchers, and policymakers will be able to make informed decisions regarding the adoption and implementation of ML techniques for regulating healthcare records. The findings of this paper revealed that ML can play an important role in efficiently directing and classifying healthcare records using different perspectives.

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