BBNSF: Blockchain-Based Novel Secure Framework Using RP2-RSA and ASR-ANN Technique for IoT Enabled Healthcare Systems

被引:13
作者
Kumar, Mohit [1 ]
Mukherjee, Priya [2 ]
Verma, Sahil [3 ]
Kavita [3 ]
Kaur, Maninder [4 ]
Singh, S. [5 ]
Kobielnik, Martyna [6 ]
Wozniak, Marcin [6 ]
Shafi, Jana [7 ]
Ijaz, Muhammad Fazal [8 ]
机构
[1] MIT ADT Univ, Dept Informat Technol, Pune 412201, India
[2] RBSPL, Bangalore 560008, India
[3] SGT Univ, Fac Comp Sci & Engn, Gurugram 122505, India
[4] Guru Gobind Singh Coll Women, Dept Comp Sci & Applicat, Chandigarh 160019, India
[5] Uttaranchal Univ, Div Res & Innovat, Dehra Dun 248007, India
[6] Silesian Tech Univ, Fac Appl Math, Kaszubska 23, PL-44100 Gliwice, Poland
[7] Prince Sattam bin Abdul Aziz Univ, Coll Arts & Sci, Dept Comp Sci, Wadi Ad Dawasir 11991, Saudi Arabia
[8] Sejong Univ, Dept Intelligent Mechatron Engn, Seoul 05006, South Korea
关键词
Internet of Medical Things (IoMT); data privacy; blockchain; security; reversed public-private keys combined Rivest-Shamir-Adleman; correlation factor-induced salp swarm optimization algorithm; data encryption; classification; INTERNET;
D O I
10.3390/s22239448
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
The wearable healthcare equipment is primarily designed to alert patients of any specific health conditions or to act as a useful tool for treatment or follow-up. With the growth of technologies and connectivity, the security of these devices has become a growing concern. The lack of security awareness amongst novice users and the risk of several intermediary attacks for accessing health information severely endangers the use of IoT-enabled healthcare systems. In this paper, a blockchain-based secure data storage system is proposed along with a user authentication and health status prediction system. Firstly, this work utilizes reversed public-private keys combined Rivest-Shamir-Adleman (RP2-RSA) algorithm for providing security. Secondly, feature selection is completed by employing the correlation factor-induced salp swarm optimization algorithm (CF-SSOA). Finally, health status classification is performed using advanced weight initialization adapted SignReLU activation function-based artificial neural network (ASR-ANN) which classifies the status as normal and abnormal. Meanwhile, the abnormal measures are stored in the corresponding patient blockchain. Here, blockchain technology is used to store medical data securely for further analysis. The proposed model has achieved an accuracy of 95.893% and is validated by comparing it with other baseline techniques. On the security front, the proposed RP2-RSA attains a 96.123% security level.
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页数:16
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