Survey: Self-Empowered Wireless Sensor Networks Security Taxonomy, Challenges, and Future Research Directions

Adil, Muhammad; Menon, Varun G.; Balasubramanian, Venki; Otaibi, Sattam Al; Song, Houbing; Jin, Zhanpeng; Farouk, Ahmed

doi:10.1109/jsen.2022.3216824

Cited by 8 publications

(3 citation statements)

References 129 publications

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“…Privacy and Security: In H-IoT, the privacy and security of patients and their data are paramount as any compromise could result in severe consequences (i.e., loss of command and control of a cobot arm used for precision surgeries may lead to its malfunctioning causing harms to person, property, loss of availability could lead to healthcare service disruption leaving critical patients who may need immediate attention vulnerable, loss of integrity would risk patients to wrong diagnosis or medication, loss of confidentiality would lead to loss of privacy, digital identity frauds, deep fakes, biohacking issues, etc.). In many cases, H-IoT uses heterogeneous networks to communicate and store data, exposing it to attack over long distances [268]. Thus, the security and privacy of data require anomaly detection, mitigation, robust encryption, and authentication techniques as mentioned in section VII-B.…”

Section: B Challengesmentioning

confidence: 99%

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

Khatun,

Memon,

Eising

et al. 2023

IEEE Access

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The Healthcare Internet-of-Things (H-IoT), commonly known as Digital Healthcare, is a datadriven infrastructure that highly relies on smart sensing devices (i.e., blood pressure monitors, temperature sensors, etc.) for faster response time, treatments, and diagnosis. However, with the evolving cyber threat landscape, IoT devices have become more vulnerable to the broader risk surface (e.g., risks associated with generative AI, 5G-IoT, etc.), which, if exploited, may lead to data breaches, unauthorized access, and lack of command and control and potential harm. This paper reviews the fundamentals of healthcare IoT, its privacy, and data security challenges associated with machine learning and H-IoT devices. The paper further emphasizes the importance of monitoring healthcare IoT layers such as perception, network, cloud, and application. Detecting and responding to anomalies involves various cyber-attacks and protocols such as Wi-Fi 6, Narrowband Internet of Things (NB-IoT), Bluetooth, ZigBee, LoRa, and 5G New Radio (5G NR). A robust authentication mechanism based on machine learning and deep learning techniques is required to protect and mitigate H-IoT devices from increasing cybersecurity vulnerabilities. Hence, in this review paper, security and privacy challenges and risk mitigation strategies for building resilience in H-IoT are explored and reported.

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Section: B Challengesmentioning

confidence: 99%

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

Khatun,

Memon,

Eising

et al. 2023

IEEE Access

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“…The metaverse is gradually transitioning from a conceptual idea to a tangible reality as various technologies continue to advance. These technologies include wearable sensors [17], non-fungible tokens (NFTs) [18], augmented reality (AR) [19][20][21], 5G connectivity [16,[22][23][24], DTs [3], blockchain [25][26][27], virtual reality (VR) [28,29], brain-computer interfaces [30], and artificial intelligence (AI) [31]. This progression has sparked global interest, leading major technology firms like "Meta" (formerly Facebook), Microsoft, Tencent, and NVIDIA to invest in its development [32].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey of Digital Twins in Healthcare in the Era of Metaverse

Turab

Jamil

2023

BioMedInformatics

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Digital twins (DTs) are becoming increasingly popular in various industries, and their potential for healthcare in the metaverse continues to attract attention. The metaverse is a virtual world where individuals interact with digital replicas of themselves and the environment. This paper focuses on personalized and precise medicine and examines the current application of DTs in healthcare within the metaverse. Healthcare practitioners may use immersive virtual worlds to replicate medical scenarios, improve teaching experiences, and provide personalized care to patients. However, the integration of DTs in the metaverse poses technical, regulatory, and ethical challenges that need to be addressed, including data privacy, standards, and accessibility. Through this examination, we aim to provide insights into the transformative potential of DTs in healthcare within the metaverse and encourage further research and development in this exciting domain.

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“…In [8,9], the authors discussed the most relevant studies associated with semisupervised approaches that, in general, use label data for anomaly detection in different applications. The authors have noted that while the trained model generally performs well in detecting anomalies, it sometimes struggles to identify new ones during the testing phase.…”

Section: Introductionmentioning

confidence: 99%

Double Deep Q-Network Next-Generation Cyber-Physical Systems: A Reinforcement Learning-Enabled Anomaly Detection Framework for Next-Generation Cyber-Physical Systems

Zhang,

Jamjoom,

Ullah

2023

Electronics

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In this work, we considered the problem of anomaly detection in next-generation cyber-physical systems (NG-CPS). For this, we used a double deep Q-network-enabled framework, where an agent was trained to detect anomalies in the traffic that does not match the behavior of the legitimate traffic at the end side. Furthermore, the proposed paradigm recognizes known and unknown anomalies by directly engaging with a simulation environment. Given that, it progressively develops its interpretation of anomalies to encompass new, previously unrecognized classes of anomalies by proactively exploring probable anomalies in data that have not been labeled. The method achieves this by concurrently optimizing the use of a limited amount of labeled abnormality data for better understanding (exploitation) and the identification of infrequent, unlabeled anomalies (exploration). During analysis, we observed that the proposed model achieves significant results in the context of average and greedy catching of anomalies in the presence of comparative models.

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Survey: Self-Empowered Wireless Sensor Networks Security Taxonomy, Challenges, and Future Research Directions

Abstract: County (UMBC)ScholarWorks@UMBC digital repository on the Maryland Shared Open Access (MD-SOAR) platform.

Cited by 8 publications

References 129 publications

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

A Comprehensive Survey of Digital Twins in Healthcare in the Era of Metaverse

Double Deep Q-Network Next-Generation Cyber-Physical Systems: A Reinforcement Learning-Enabled Anomaly Detection Framework for Next-Generation Cyber-Physical Systems

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