Improved Handover Authentication in Fifth-Generation Communication Networks Using Fuzzy Evolutionary Optimisation with Nanocore Elements in Mobile Healthcare Applications

Divakaran, J.; Prashanth, S. K.; Mohammad, Gouse Baig; Shitharth, S.; Mohanty, Sachi Nandan; Chakrapani, Arvind; Srihari, K.; R., Yasir Abdullah; Sundramurthy, Venkatesa Prabhu

doi:10.1155/2022/2500377

Cited by 12 publications

(7 citation statements)

References 27 publications

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“…An evolutionary approach that utilizes the fuzzy model was developed by Divakaran et al [23] for handover regulation and key management to enhance authentication performance. Delays and complexities have been minimized when authenticating 5G networks based on nanocore technology.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Handover Authentication Mechanism Using Deep Learning in SDNBased 5G HetNets

2023

IJIES

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The fifth generation (5G) networks are a popular standard that carries effective skills to conquer the tests of next generation wireless networks. Also, the 5G systems can support high data traffic by rendering high throughput and low latency towards the massively connected nodes. Here, handover is highly significant for data processing, portability and real time data creation in mobile technologies. With the 5G entrance, the cellular network has become a completely heterogeneous network (HetNet). The Software Defined Network (SDN) concept is used in 5G HetNets for better mobility management. Most existing research works have concentrated on handover authentication, but those works are often prone to re-authentication issues and increased handover delay. Therefore, to overcome the reauthentication process and provide users with better services, a novel handover authentication mechanism using deep learning (DHan_Auth) is proposed. Initially, the 5G data attack and normal data are collected, and the malicious and non-malicious users are classified using the Convolution Stacked long short term memory network model (Conv_SLSTM). To improve handover process and resist network attacks, only the non-malicious user data are authenticated through the key generation and the 5G Handover-Authentication and Key Agreement (5G_AKA) protocol. The process of encryption and decryption are performed using Extended Elliptic curve cryptography (Ex_ECC). When simulating with the PYTHON platform, performance such as handover latency, accuracy and precision are analyzed. The handover latency of the proposed model is 11.8 seconds to 200 nodes, while the classification accuracy in categorizing the malicious and non-malicious users reaches 98.98%.

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Section: Related Workmentioning

confidence: 99%

An Efficient Handover Authentication Mechanism Using Deep Learning in SDNBased 5G HetNets

2023

IJIES

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“…Fuzzy evolutionary model to improve transfer authentication was proposed in [23], where fuzzy logic used to provide the assignment and key management authentication performance in 5G networks based on nanocore technology. The proposed model aimed to minimize the complexity and delays during authentication in 5G networks and mitigated authentication attacks when it is trained properly with the related attack datasets.…”

Section: Authentication Of S-health Using 5g Networkmentioning

confidence: 99%

Towards fostering the role of 5G networks in the field of digital health

Turab,

Al-Nabulsi,

Abu-Alhaija

et al. 2023

IJECE

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<div align="center"><span lang="EN-US">A typical healthcare system needs further participation with patient monitoring, vital signs sensors and other medical devices. Healthcare moved from a traditional central hospital to scattered patients. Healthcare systems receive help from emerging technology innovations such as fifth generation (5G) communication infrastructure: internet of things (IoT), machine learning (ML), and artificial intelligence (AI). Healthcare providers benefit from IoT capabilities to comfort patients by using smart appliances that improve the healthcare level they receive. These IoT smart healthcare gadgets produce massive data volume. It is crucial to use very high-speed communication networks such as 5G wireless technology with the increased communication bandwidth, data transmission efficiency and reduced communication delay and latency, thus leading to strengthen the precise requirements of healthcare big data utilities. The adaptation of 5G in smart healthcare networks allows increasing number of IoT devices that supplies an augmentation in network performance. This paper reviewed distinctive aspects of internet of medical things (IoMT) and 5G architectures with their future and present sides, which can lead to improve healthcare of patients in the near future.</span></div>

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“…e major requirements of RHM systems are a variety of sensors and communication devices [64]. Widely used sensors are wearable sensors (wireless or external wearable sensors), vision-based sensors, body sensor networks (BSN), and communication devices are categorized into two types, i.e., short-range communication devices and long-range communication devices [65][66][67][68][69][70][71] as described in Tables 4 and 3, demonstrating various sensors used in healthcare applications for acquiring real-time data, and the complete range of all communication devices is also described in Table 5.…”

Section: Contrast Media and Molecular Imagingmentioning

confidence: 99%

A Comprehensive Review on Smart Health Care: Applications, Paradigms, and Challenges with Case Studies

Raoof

Durai

2022

Contrast Media & Molecular Imaging

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Growth and advancement of the Deep Learning (DL) and the Internet of Things (IoT) are figuring out their way over the modern contemporary world through integrating various technologies in distinct fields viz, agriculture, manufacturing, energy, transportation, supply chains, cities, healthcare, and so on. Researchers had identified the feasibility of integrating deep learning, cloud, and IoT to enhance the overall automation, where IoT may prolong its application area through utilizing cloud services and the cloud can even prolong its applications through data acquired by IoT devices like sensors and deep learning for disease detection and diagnosis. This study explains a summary of various techniques utilized in smart healthcare, i.e., deep learning, cloud-based-IoT applications in smart healthcare, fog computing in smart healthcare, and challenges and issues faced by smart healthcare and it presents a wider scope as it is not intended for a particular application such aspatient monitoring, disease detection, and diagnosing and the technologies used for developing this smart systems are outlined. Smart health bestows the quality of life. Convenient and comfortable living is made possible by the services provided by smart healthcare systems (SHSs). Since healthcare is a massive area with enormous data and a broad spectrum of diseases associated with different organs, immense research can be done to overcome the drawbacks of traditional healthcare methods. Deep learning with IoT can effectively be applied in the healthcare sector to automate the diagnosing and treatment process even in rural areas remotely. Applications may include disease prevention and diagnosis, fitness and patient monitoring, food monitoring, mobile health, telemedicine, emergency systems, assisted living, self-management of chronic diseases, and so on.

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Improved Handover Authentication in Fifth-Generation Communication Networks Using Fuzzy Evolutionary Optimisation with Nanocore Elements in Mobile Healthcare Applications

Cited by 12 publications

References 27 publications

An Efficient Handover Authentication Mechanism Using Deep Learning in SDNBased 5G HetNets

An Efficient Handover Authentication Mechanism Using Deep Learning in SDNBased 5G HetNets

Towards fostering the role of 5G networks in the field of digital health

A Comprehensive Review on Smart Health Care: Applications, Paradigms, and Challenges with Case Studies

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