Innovative Data Security Framework for IoMT Using Lightweight Cryptography and RDWT Steganography
Keywords:
Internet of Medical Things, real-time diagnosis, remote patient monitoring, realtime medicine prescriptions, medical information security, lightweight cryptographyAbstract
The fusion of the Internet of Things (IoT) with medical systems, termed the Internet of Medical Things (IoMT), facilitates critical medical functions such as instant diagnosis, remote patient monitoring, and real-time prescription management. However, a significant challenge in healthcare services revolves around ensuring the security and privacy of medical data within IoMT platforms. This study focuses on integration of lightweight cryptography techniques with a steganography model to safeguard medical information. Initially, medical data undergoes segmentation into even and odd characters, with elliptic curve cryptography (ECC) applied to encrypt even characters and Feistel Block Cipher (FBC) to encrypt odd characters. Subsequently, a redundant discrete wavelet transforms (RDWT) based steganography technique conceals the encrypted data within a cover image. Simulation results demonstrate that the proposed method achieves superior resilience and imperceptibility in terms of metrics like Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Mean Square Error (MSE) compared to existing methods. Furthermore, the proposed approach also boasts reduced computational overhead compared to traditional techniques.
Downloads
References
. Yaacoub, Jean-Paul A., et al. "Securing internet of medical things systems: Limitations,
issues and recommendations." Future Generation Computer Systems 105 (2020): 581-606.
. Ullah, Ata, et al. "Secure healthcare data aggregation and transmission in IoT—A
survey." IEEE Access 9 (2021): 16849-16865.
. Kaabi, Rana, Hassan Fakhruldeen, and Karrar Alhamami. "The Status, Challenges, and
Future Trends of Advanced Crypto Algorithms for Wireless Network Security: an
Overview." (2021).
. Qiu, Han, et al. "Secure health data sharing for medical cyber-physical systems for the
healthcare 4.0." IEEE journal of biomedical and health informatics 24.9 (2020): 2499-2505.
. Papaioannou, Maria, et al. "A survey on security threats and countermeasures in internet of
medical things (IoMT)." Transactions on Emerging Telecommunications
Technologies (2020): e4049.
. Sun, Yingnan, Frank P-W. Lo, and Benny Lo. "Security and privacy for the internet of
medical things enabled healthcare systems: A survey." IEEE Access 7 (2019): 183339-
. Kagita, Mohan Krishna, et al. "A review on security and privacy of internet of medical
things." Intelligent Internet of Things for Healthcare and Industry. Springer, Cham, 2022.
-187.
. Ray, Partha Pratim, Dinesh Dash, and Neeraj Kumar. "Sensors for internet of medical things:
State-of-the-art, security and privacy issues, challenges and future directions." Computer
Communications 160 (2020): 111-131.
. Thamilarasu, Geethapriya, Adedayo Odesile, and Andrew Hoang. "An intrusion detection
system for internet of medical things." IEEE Access 8 (2020): 181560-181576.
. Parah, S. A., Kaw, J. A., Bellavista, P., Loan, N. A., Bhat, G. M., Muhammad, K., & de
Albuquerque, V. H. C. (2020). Efficient security and authentication for edge-based internet
of medical things. IEEE Internet of Things Journal, 8(21), 15652-15662.
. Huang, Xucheng, and Shah Nazir. "Evaluating security of internet of medical things using
the analytic network process method." Security and Communication Networks 2020 (2020).
. Kumar, Randhir, and Rakesh Tripathi. "Towards design and implementation of security and
privacy framework for internet of medical things (iomt) by leveraging blockchain and ipfs
technology." The Journal of Supercomputing 77.8 (2021): 7916-7955.
. Manimurugan, S., Al-Mutairi, S., Aborokbah, M. M., Chilamkurti, N., Ganesan, S., & Patan,
R. (2020). Effective attack detection in internet of medical things smart environment using a
deep belief neural network. IEEE Access, 8, 77396-77404.
. Alsubaei, F., Abuhussein, A., Shandilya, V., & Shiva, S. (2019). IoMT-SAF: Internet of
medical things security assessment framework. Internet of Things, 8, 100123.
. Allahham, M. S., Abdellatif, A. A., Mohamed, A., Erbad, A., Yaacoub, E., & Guizani, M.
(2020). I-SEE: Intelligent, Secure, and Energy-Efficient Techniques for Medical Data
Transmission Using Deep Reinforcement Learning. IEEE Internet of Things Journal, 8(8),
-6468.
. Stoyanov, Bozhidar, and Borislav Stoyanov. "BOOST: Medical image steganography using
nuclear spin generator." Entropy 22.5 (2020): 501.
. Bansal, Ritesh, Chander Kumar Nagpal, and Shailender Gupta. "An efficient hybrid security
mechanism based on chaos and improved BPCS." Multimedia Tools and Applications 77.6
(2018): 6799-6835.
. Dhall, Sangeeta, Rinku Sharma, and Shailender Gupta. "A multi-level steganography
mechanism using quantum chaos encryption." Multimedia Tools and Applications 79.3
(2020): 1987-2012.
. Pandey, Hari Mohan. "Secure medical data transmission using a fusion of bit mask oriented
genetic algorithm, encryption and steganography." Future Generation Computer
Systems 111 (2020): 213-225.
. Prasanalakshmi, B., et al. "Improved authentication and computation of medical data
transmission in the secure IoT using hyperelliptic curve cryptography." The Journal of
Supercomputing 78.1 (2022): 361-378.
. Panwar, Priya, Sangeeta Dhall, and Shailender Gupta. "A multilevel secure information
communication model for healthcare systems." Multimedia Tools and Applications 80.5
(2021): 8039-8062.
. Elhoseny, Mohamed, et al. "Secure medical data transmission model for IoT-based
healthcare systems." Ieee Access 6 (2018): 20596-20608.
. Denis, R., & Madhubala, P. (2021). Hybrid data encryption model integrating multiobjective adaptive genetic algorithm for secure medical data communication over cloudbased healthcare systems. Multimedia Tools and Applications, 80(14), 21165-21202.
. Huang, Haiping, et al. "Private and secured medical data transmission and analysis for
wireless sensing healthcare system." IEEE Transactions on Industrial Informatics 13.3
(2017): 1227-1237.
. Rani, S. Sheeba, et al. "Optimal users based secure data transmission on the internet of
healthcare things (IoHT) with lightweight block ciphers." Multimedia Tools and
Applications 79.47 (2020): 35405-35424.
. Reddy, V. P. C., & Gurrala, K. K. (2022). Joint DR-DME classification using deep learningCNN based modified grey-wolf optimizer with variable weights. Biomedical Signal
Processing and Control, 73, 103439.
. Oh, Yujin, Sangjoon Park, and Jong Chul Ye. "Deep learning COVID-19 features on CXR
using limited training data sets." IEEE transactions on medical imaging 39.8 (2020): 2688-
. Tabik, Siham, et al. "COVIDGR dataset and COVID-SDNet methodology for predicting
COVID-19 based on chest X-ray images." IEEE journal of biomedical and health
informatics 24.12 (2020): 3595-3605.
. Varma, P. B. S., Paturu, S., Mishra, S., Rao, B. S., Kumar, P. M., & Krishna, N. V. SLDCNet:
Skin lesion detection and classification using full resolution convolutional network‐based
deep learning CNN with transfer learning. Expert Systems, e12944.
. Gottumukkala, VSSP Raju, N. Kumaran, and V. Chandra Sekhar. "BLSNet: Skin lesion
detection and classification using broad learning system with incremental learning
algorithm." Expert Systems: e12938.
Downloads
Published
Issue
Section
License
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
