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JK, Periasamy, L, Selvam, M, Anuradha, Kennady, R. (1402). A fuzzy optimal lightweight convolutional neural network for deduplication detection in cloud server. نشریه علمی دانشگاه سیستان و بلوچستان, 21(1), 33-49. doi: 10.22111/ijfs.2023.45797.8070
Periasamy JK; Selvam L; Anuradha M; R Kennady. "A fuzzy optimal lightweight convolutional neural network for deduplication detection in cloud server". نشریه علمی دانشگاه سیستان و بلوچستان, 21, 1, 1402, 33-49. doi: 10.22111/ijfs.2023.45797.8070
JK, Periasamy, L, Selvam, M, Anuradha, Kennady, R. (1402). 'A fuzzy optimal lightweight convolutional neural network for deduplication detection in cloud server', نشریه علمی دانشگاه سیستان و بلوچستان, 21(1), pp. 33-49. doi: 10.22111/ijfs.2023.45797.8070
JK, Periasamy, L, Selvam, M, Anuradha, Kennady, R. A fuzzy optimal lightweight convolutional neural network for deduplication detection in cloud server. نشریه علمی دانشگاه سیستان و بلوچستان, 1402; 21(1): 33-49. doi: 10.22111/ijfs.2023.45797.8070

A fuzzy optimal lightweight convolutional neural network for deduplication detection in cloud server

Iranian Journal of Fuzzy Systems
دوره 21، شماره 1، فروردین و اردیبهشت 2024، صفحه 33-49    اصل مقاله (1.23 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22111/ijfs.2023.45797.8070
نویسندگان
Periasamy JK* 1؛ Selvam L2؛ Anuradha M3؛ R Kennady4
1Sri Sairam Engineering College, chennai
2K S R Institute for Engineering and Technology
3S A Engineering college
4Rajalakshmi Institute of Technology Kuthambakkam
چکیده
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.
کلیدواژه‌ها
Security؛ Encryption؛ Key generation؛ Lightweight convolutional neural network؛ Fuzzy logic system؛ Tuna swarm optimization algorithm
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