Iot Based Alzheimer’s Disease Diagnosis Model for Providing Security Using Light Weight Hybrid Cryptography
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Abstract
Security in the Internet of things (IoT) is a broad yet active research area that focuses on securing the sensitive data being circulated in the network. The data involved in the IoT network comes from various organizations, hospitals, etc., that require a higher range of security from attacks and breaches. The common solution for security attacks is using traditional cryptographic algorithms that can protect the content through encryption and decryption operations. The existing solutions are suffering from major drawbacks, including computational complexities, time and space complexities, slower encryption, etc. Therefore, to overcome such drawbacks, this paper introduces an efficient light weight cryptographic mechanism to secure the images of Alzheimer’s disease (AD) being transmitted in the network. The mechanism involves major stages such as edge detection, key generation, encryption, and decryption. In the case of edge detection, the edge maps are detected using the Prewitt edge detection technique. Then the hybrid elliptic curve cryptography (HECC) algorithm is proposed to encrypt and secure the images being transmitted in the network. For encryption, the HECC algorithm combines blowfish with the elliptic curve algorithm to attain a higher range of security. Another significant advantage of the proposed method is selecting the ideal private key, which is achieved using the enhanced seagull optimization (ESO) algorithm. The proposed work has been tested in the Python tool, and the performance is evaluated with the Alzheimer’s dataset, and the outcomes proved its efficacy over the compared methods.
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C.R. Su, J. Hajiyev, C.J. Fu, K.C. Kao, C.H. Chang and C.T. Chang, “A novel framework for a remote patient monitoring (RPM) system with abnormality detection”. Health Policy and Technology, vol. 8, no. 2, pp.157-170, 2019.
M.M. Salim, I. Kim, U. Doniyor, C. Lee and J.H. Park, “Homomorphic Encryption Based Privacy-Preservation for IoMT”. Applied Sciences, vol. 11, no. 18, pp.8757, 2021.
R.O. Ogundokun, J.B. Awotunde, E.A. Adeniyi and F.E. Ayo, “Crypto-Stegno based model for securing medical information on IOMT platform”. Multimedia tools and applications, vol. 80, no. 21, pp.31705-31727, 2021.
M.K. Hasan, T.M. Ghazal, R.A. Saeed, B. Pandey, H. Gohel, A.A. Eshmawi, S. Abdel?Khalek and H.M. Alkhassawneh, “A review on security threats, vulnerabilities, and counter measures of 5G enabled Internet?of?Medical?Things”. IET Communications, vol. 16, no. 5, pp.421-432, 2022.
M.A. Mohammed, D.A. Ibrahim and K.H. Abdulkareem, “Bio-inspired robotics enabled schemes in blockchain-fog-cloud assisted IoMT environment”. Journal of King Saud University-Computer and Information Sciences 2021.
K.V. Greeshma and J.V. Gripsy, “A Review on Classification and Retrieval of Biomedical Images Using Artificial Intelligence”. The Fusion of Internet of Things, Artificial Intelligence, and Cloud Computing in Health Care, pp.47-66, 2021.
K. Fuchs, W.C. Harding, F. Hudson and M. Parker, “TIPPSS for Facilitating Connected Healthcare Interoperability”. In Mobile Medicine. Productivity Press pp. 269-279, 2021.
K.Y. Tehseen, N. Imran, A.S. Qazi, W. Ahmad, F. Arslan, A. Ijaz and M. Ali, “Transformative Effects of COVID-19 on Global Economy and Internet of Medical Things (IoMT): Current Vision, Role and Applications”. International J. on Emerging Technologies, vol. 12, no. 2, pp.66-76, 2021.
N. Jain and S.S. Chauhan, “Novel Approach Transforming Stream Cipher to Block Cipher”. In 2021 International Conference on Technological Advancements and Innovations (ICTAI) IEEE pp. 182-187, 2021.
A. Sevin and A.A.O. Mohammed, “A survey on software implementation of lightweight block ciphers for IoT devices”. Journal of Ambient Intelligence and Humanized Computing, pp. 1-15, 2021.
M.K. Hasan, M. Shafiq, S. Islam, B. Pandey, Y.A.B. El-Ebiary, N.S. Nafi, R.C. Rodriguez and D.E. Vargas, “Lightweight cryptographic algorithms for guessing attack protection in complex Internet of things applications”. Complexity, vol. 2021, 2021.
H. Xu, K. Thakur, A.S. Kamruzzaman and M.L. Ali, “Applications of Cryptography in Database: A Review”. In 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) pp. 1-6, 2021.
A.K. Bermani, T.A. Murshedi and Z.A. Abod, “A hybrid cryptography technique for data storage on cloud computing”. Journal of Discrete Mathematical Sciences and Cryptography, vol. 24, no. 6, pp.1613-1624, 2021.
S. Kumar, G. Karnani, M.S. Gaur and A. Mishra, “Cloud security using hybrid cryptography algorithms”. In 2021 2nd International conference on intelligent engineering and management (ICIEM) IEEE pp. 599-604, 2021.
P. William, A. Choubey, G.S. Chhabra, R. Bhattacharya, K. Vengatesan and S. Choubey, “Assessment of Hybrid Cryptographic Algorithm for Secure Sharing of Textual and Pictorial Content”. In 2022 International Conference on Electronics and Renewable Systems (ICEARS) IEEE, pp. 918-922, 2022.
M.U. Bokhari, Q.M. Shallal and Y.K. Tamandani, “Reducing the required time and power for data encryption and decryption using K-NN machine learning”. IETE Journal of Research, vol. 65, no. 2, pp. 227-235, 2019.
R. Abid, C. Iwendi, A.R. Javed, M. Rizwan, Z. Jalil, J.H. Anajemba and C. Biamba, “An optimized homomorphic CRT-RSA algorithm for secure and efficient communication”. Personal and Ubiquitous Computing, pp.1-14, 2021.
Y. Ding, F. Tan, Z. Qin, M. Cao, K.K.R. Choo and Z. Qin, “DeepKeyGen: a deep learning-based stream cipher generator for medical image encryption and decryption”. IEEE Transactions on Neural Networks and Learning Systems vol. 2021, 2020.
S. Chirakkarottu and S. Mathew, “A novel encryption method for medical images using 2D Zaslavski map and DNA cryptography”. SN Applied Sciences, vol. 2, no. 1, pp.1-10, 2019.
Y. Chen, C. Tang and R. Ye, “Cryptanalysis and improvement of medical image encryption using high-speed scrambling and pixel adaptive diffusion”. Signal Processing, vol. 167, pp.107286, 2020.
Y. Ding, G. Wu, D. Chen, N. Zhang, L. Gong, M. Cao and Z. Qin, DeepEDN: a deep-learning-based image encryption and decryption network for Internet of medical things. IEEE Internet of Things Journal, vol. 8, no. 3, pp.1504-1518, 2020.
W. El-Shafai, F. Khallaf, E.S.M. El-Rabaie and F.E.A. El-Samie, “Robust medical image encryption based on DNA-chaos cryptosystem for secure telemedicine and healthcare applications”. Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 10, pp.9007-9035, 2021.
C. Lakshmi, K. Thenmozhi, J.B.B. Rayappan, S. Rajagopalan, R. Amirtharajan and N. Chidambaram, “Neural-assisted image-dependent encryption scheme for medical image cloud storage”. Neural Computing and Applications, vol. 33, no. 12, pp.6671-6684, 2021.
A.S. Ahmed and H.A. Salah, “The IoT and registration of MRI brain diagnosis based on genetic algorithm and convolutional neural network.” Indonesian Journal of Electrical Engineering and Computer Science vol. 25, no. 1, pp. 273, 2022.
S. Arunkumar, V. Subramaniyaswamy, V. Vijayakumar, Naveen Chilamkurti, and R. Logesh. “SVD-based robust image steganographic scheme using RIWT and DCT for secure transmission of medical images.” Measurement vol. 139, pp. 426-437, 2019.
S. Borra, R. Thanki, N. Dey and K. Borisagar, “Secure transmission and integrity verification of color radiological images using fast discrete curvelet transform and compressive sensing”. Smart Health vol. 12, pp. 35-48, 2019.
W.I. Khedr, “A new efficient and configurable image encryption structure for secure transmission.” Multimedia Tools and Applications vol. 79, no. 23, pp. 16797-16821, 2020.
S.P. Raja, “Joint medical image compression–encryption in the cloud using multiscale transform-based image compression encoding techniques.” S?dhan? vol. 44, no. 2, pp. 1-10, 2019.