A Survey on IoT Vulnerability Discovery

Ban, Xinbo; Ding, Ming; Liu, Shigang; Chen, Chao; Zhang, Jun

doi:10.1007/978-3-031-23020-2_15

Cited by 2 publications

(2 citation statements)

References 25 publications

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“…Ban et al [19] summarized the typical research methods used in IoT security. Every study that finds a hole in the Internet of Things (IoT) infrastructure was carefully examined using a standard research method.…”

Section: Machine Learning Models In Iomtmentioning

confidence: 99%

Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT)

et al. 2023

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Internet of Medical Things (IoMT) is an ecosystem composed of connected electronic items such as small sensors/actuators and other cyber-physical devices (CPDs) in medical services. When these devices are linked together, they can support patients through medical monitoring, analysis, and reporting in more autonomous and intelligent ways. The IoMT devices; however, often do not have sufficient computing resources onboard for service and security assurance while the medical services handle large quantities of sensitive and private health-related data. This leads to several research problems on how to improve security in IoMT systems. This paper focuses on quantum machine learning to assess security vulnerabilities in IoMT systems. This paper provides a comprehensive review of both traditional and quantum machine learning techniques in IoMT vulnerability assessment. This paper also proposes an innovative fused semi-supervised learning model, which is compared to the state-of-the-art traditional and quantum machine learning in an extensive experiment. The experiment shows the competitive performance of the proposed model against the state-of-the-art models and also highlights the usefulness of quantum machine learning in IoMT security assessments and its future applications.

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Section: Machine Learning Models In Iomtmentioning

confidence: 99%

Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT)

et al. 2023

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“…With an increase in IoT vulnerability discovery and exploitation, research into IoT vulnerability detection and mitigation has recently received unprecedented attention. This has resulted in a significant number of published academic articles and survey papers addressing various aspects of IoT security challenges [16][17][18][19][20][21]. In particular, there is a growing body of literature on IoT vulnerability assessment and quantification frameworks.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Consumer IoT Device Vulnerability Quantification Frameworks

Baho

Abawajy

2023

Electronics

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The increasing deployment of Internet of Things (IoT) devices in mission-critical systems has made them more appealing to attackers. Cyberattacks on IoT devices have the potential to expose sensitive data, disrupt operations, and even endanger lives. As a result, IoT security has recently gained traction in both industry and academia. However, no research has examined existing IoT vulnerability assessment frameworks in a systematic and comprehensive manner. To address this gap, this paper systematically reviews and analyses the research challenges and state-of-the-art IoT vulnerability assessment frameworks while taking into account both breadth and depth. The study provides insight into current IoT vulnerability assessment approaches, which is useful for ongoing efforts to characterise cybersecurity risks and manage IoT vulnerabilities. It will be of interest to a spectrum of readers, including those in the IoT research community, researchers in cybersecurity, risk and vulnerability management professionals, and others. By offering the latest perspective on the present IoT vulnerability assessment techniques, this study will raise IoT security awareness and facilitate research into IoT vulnerability assessment methodologies. The knowledge provided by this study will also be beneficial to future academics who are interested in the issues and solutions surrounding IoT security. The report also assists in understanding the research direction in IoT vulnerability assessment approaches, making it beneficial for those looking to create new methods for determining IoT vulnerabilities.

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A Survey on IoT Vulnerability Discovery

Cited by 2 publications

References 25 publications

Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT)

Quantum Machine Learning for Security Assessment in the Internet of Medical Things (IoMT)

Analysis of Consumer IoT Device Vulnerability Quantification Frameworks

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