Document Type : Research Paper
Authors
1 Sri Sairam Engineering College, chennai
2 K S R Institute for Engineering and Technology
3 S A Engineering college
4 Rajalakshmi Institute of Technology Kuthambakkam
Abstract
Nowadays the cloud computing environment is widely utilized for transmitting and receiving data securely. Inorder to secure the data the encryption method is used but still due to some limitations the security process is diminished. Therefore, this paper proposes a new algorithm to provide better security while transmitting data through the network. At first, the sensitivity of data is determined using a lightweight convolutional neural network (LWCNN) model which is used to categorize the unclassified data into two categories normal sensitive data and highly sensitive data. After determining the level of data sensitivity, the encryption process is performed further. The efficient hash function-based duplication detection approach is employed to maintain confidential information before outsourcing it to a cloud server. Subsequently, the ideal keys are generated for each data based on its sensitivity level using the proposed fuzzy tuna swarm (FTS) algorithm. Finally, the data is encrypted by converting plain text into ciphertext which is only visible to authorized users. The experimental results show that the LWCNN model utilized for data sensitivity classification achieved 94% accuracy and the FTS algorithm proposed for optimal key generation took much less communication time of about 1800μs than other compared techniques.
Keywords
- Security
- Encryption
- Key generation
- Lightweight convolutional neural network
- Fuzzy logic system
- Tuna swarm optimization algorithm
Main Subjects
patterns as chunks divisors, Journal of King Saud University-Computer and Information Sciences, 34(7) (2022),
4669-4678.
Hyperchaotic electrical circuit, Journal of Electrical Engineering and Technology, 15(3) (2020), 1413-1429.
scheme based on genetic algorithm and residual numbers, IEEE Access, 8 (2020), 100438-100447.
decryption using K-NN machine learning, IETE Journal of Research, 65(2) (2019), 227-235.
Access, 8 (2020), 221897-221917.
for medical image encryption and decryption, IEEE Transactions on Neural Networks and Learning Systems, 2021.
and decryption network for internet of medical things, IEEE Internet of Things Journal, 8(3) (2020), 1504-1518.
decryption for edge intelligent IoV, IEEE Transactions on Vehicular Technology, 69(11) (2020), 13784-13795.
for speech signals encryption and decryption, Procedia Computer Science, 167 (2020), 1028-1037.
to hash framework for remote sensing image retrieval, IEEE Transactions on Geoscience and Remote Sensing, 58(10)
(2020), 7331-7345.
cloud computing, Journal of Real-Time Image Processing, 17(1) (2020), 175-184.
homomorphic encryption scheme, IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 28(2) (2019),
353-362.
DNA encoding technique, Multimedia Tools and Applications, 79(43) (2020), 31739-31757.
algorithm in cloud environment, Neural Computing and Applications, 32(2) (2020), 485-494.
deduction and reduction, Concurrency and Computation: Practice and Experience, 33(3) (2021), 5213.
based encryption for digital image security, Concurrency and Computation: Practice and Experience, 32(4) (2020),
5122.
and improved ant colony optimization, IEEE Access, 8 (2020), 62107-62115.
with extreme resemblance detection, Connection Science, 33(3) (2021), 576-604.
heuristic algorithm for global optimization, Computational Intelligence and Neuroscience, 2021.
Electrical Engineering and Computer Science, 18(2) (2020), 774-781.
)