Security, Privacy and Trust Issues in Internet of Things and Machine Learning Based e-Healthcare

Kute, Shruti; Tyagi, Amit Kumar; Aswathy, S. U.

doi:10.1007/978-981-16-6542-4_15

Cited by 19 publications

(5 citation statements)

References 41 publications

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“…Authors in [13] emphasize the application of machine learning techniques to achieve confidentiality and integrity in IoTbased health monitoring devices. The study addresses privacy concerns and highlights the importance of securing sensitive health data generated by IoT devices.…”

Section: B Ml-based Idsmentioning

confidence: 99%

Enhancing Security of Host-Based Intrusion Detection Systems for the Internet of Things

Nallakaruppan,

Somayaji,

Fuladi

et al. 2024

IEEE Access

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The Internet of Things (IoT) infrastructure enables smart devices to learn, think, speak and perform. The facilities of the IoT devices can be enhanced to support an intelligent application through technologies like fog computing, smart networks, federated learning or explainable artificial intelligence infrastructures. In all these cases networking of IoT devices becomes inevitable. Where-ever there exists a network, a threat to the network infrastructure is also possible. The proposed work classifies various attacks on the hosts with the support of proven machine learning (ML) algorithms. This work performs the comparative analysis of all these classification parameters of the machine learning algorithms with the use of fuzzy-based recommendation systems. This work also lists out various incidents of intrusions on the IoT hosts in appropriate layers of the interface and proposes an efficient algorithm and framework to overcome the occurrences of the intrusions on the host side. In particular, we propose an effective security framework to deal with the intrusions that can deteriorate the host-based systems. The ranking of the algorithms is evaluated using fuzzy-based recommendation systems such as TOPSIS, VIKOR, MORA, WASPAS. The ensemble of machine learning algorithms such as Decision Tree, Lite Gradient Boost, Xtra Gradient Boost and Random Forest provide better values of accuracy (around 99%) with higher precision, re-call and F1scores, thus proving their efficacy for intrusion detection in IoT networks.

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Section: B Ml-based Idsmentioning

confidence: 99%

Enhancing Security of Host-Based Intrusion Detection Systems for the Internet of Things

Nallakaruppan,

Somayaji,

Fuladi

et al. 2024

IEEE Access

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“…Kute, Shruti Suhas et al [ 33 ] provided a study of cutting-edge research involving the IoT in healthcare, particularly on obesity, overweight, and persistent degenerative illnesses. Secrecy, integrity, authentication, access, trust, validation, information management, and storage and availability issues must be resolved for IoT in real-world applications.…”

Section: Related Workmentioning

confidence: 99%

Managing Security of Healthcare Data for a Modern Healthcare System

Almalawi

Khan

Alsolami

et al. 2023

Sensors

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The advent of Artificial Intelligence (AI) and the Internet of Things (IoT) have recently created previously unimaginable opportunities for boosting clinical and patient services, reducing costs and improving community health. Yet, a fundamental challenge that the modern healthcare management system faces is storing and securely transferring data. Therefore, this research proposes a novel Lionized remora optimization-based serpent (LRO-S) encryption method to encrypt sensitive data and reduce privacy breaches and cyber-attacks from unauthorized users and hackers. The LRO-S method is the combination of hybrid metaheuristic optimization and improved security algorithm. The fitness functions of lion and remora are combined to create a new algorithm for security key generation, which is provided to the serpent encryption algorithm. The LRO-S technique encrypts sensitive patient data before storing it in the cloud. The primary goal of this study is to improve the safety and adaptability of medical professionals’ access to cloud-based patient-sensitive data more securely. The experiment’s findings suggest that the secret keys generated are sufficiently random and one of a kind to provide adequate protection for the data stored in modern healthcare management systems. The proposed method minimizes the time needed to encrypt and decrypt data and improves privacy standards. This study found that the suggested technique outperformed previous techniques in terms of reducing execution time and is cost-effective.

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“…This involves implementing robust security measures to protect PHI from unauthorized access and disclosure. This includes implementing access controls, encryption, and firewalls to prevent data breaches and cyber-attacks [2]. Additionally, healthcare organizations must develop policies and procedures for securely transmitting and storing PHI in the cloud.…”

Section: Security Managementmentioning

confidence: 99%

Security and Privacy Challenges Hindering the Adoption of E-Healthcare Systems

Laboso

2023

Int. J. Res. Publ. Rev.

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The adoption of e-healthcare systems is significantly hindered by privacy and security concerns, as exposure and abuse of personal health information can lead to serious privacy breaches for patients. Patients may suffer from unequal treatment, such as termination of employment or insurance if their deteriorating health condition becomes known to their employers. Despite the general assumption that the cloud operates under the honest-but-curious model and can execute protocol specifications perfectly, it is still vulnerable to extracting a patient's private PHI during data transmission and storage. This paper highlights the potential risks associated with such systems, including the possibility of inequitable treatment, termination of contracts, and insurance policies. The paper also discusses the use of cloud-based systems and the challenges they present in terms of security management and scalability. It concludes by stressing the importance of addressing these issues to develop a more robust e-healthcare system.

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Security, Privacy and Trust Issues in Internet of Things and Machine Learning Based e-Healthcare

Cited by 19 publications

References 41 publications

Enhancing Security of Host-Based Intrusion Detection Systems for the Internet of Things

Enhancing Security of Host-Based Intrusion Detection Systems for the Internet of Things

Managing Security of Healthcare Data for a Modern Healthcare System

Security and Privacy Challenges Hindering the Adoption of E-Healthcare Systems

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