Vol.69, No.1, 2021, pp.1183-1200, doi:10.32604/cmc.2021.017199
OPEN ACCESS
ARTICLE
Cryptographic Based Secure Model on Dataset for Deep Learning Algorithms
  • Muhammad Tayyab1,*, Mohsen Marjani1, N. Z. Jhanjhi1, Ibrahim Abaker Targio Hashim2, Abdulwahab Ali Almazroi3, Abdulaleem Ali Almazroi4
1 School of Computer Science and Engineering (SCE), Taylor’s University Lake-Side Campus, Subang Jaya, 47500, Malaysia
2 Department of Computer Science, College of Computing and Informatics, University of Sharjah, Sharjah, 27272, UAE
3 University of Jeddah, College of Computing and Information Technology at Khulais, Department of Information Technology, Jeddah, Saudi Arabia
4 Department of Computer Science, Rafha Community College, Northern Border University, Arar, 91431, Saudi Arabia
* Corresponding Author: Muhammad Tayyab. Email:
Received 23 January 2021; Accepted 05 April 2021; Issue published 04 June 2021
Abstract
Deep learning (DL) algorithms have been widely used in various security applications to enhance the performances of decision-based models. Malicious data added by an attacker can cause several security and privacy problems in the operation of DL models. The two most common active attacks are poisoning and evasion attacks, which can cause various problems, including wrong prediction and misclassification of decision-based models. Therefore, to design an efficient DL model, it is crucial to mitigate these attacks. In this regard, this study proposes a secure neural network (NN) model that provides data security during model training and testing phases. The main idea is to use cryptographic functions, such as hash function (SHA512) and homomorphic encryption (HE) scheme, to provide authenticity, integrity, and confidentiality of data. The performance of the proposed model is evaluated by experiments based on accuracy, precision, attack detection rate (ADR), and computational cost. The results show that the proposed model has achieved an accuracy of 98%, a precision of 0.97, and an ADR of 98%, even for a large number of attacks. Hence, the proposed model can be used to detect attacks and mitigate the attacker motives. The results also show that the computational cost of the proposed model does not increase with model complexity.
Keywords
Deep learning (DL); poisoning attacks; evasion attacks; neural network; hash functions SHA512; homomorphic encryption scheme
Cite This Article
M. Tayyab, M. Marjani, N. Z. Jhanjhi, I. Abaker, A. A. Almazroi et al., "Cryptographic based secure model on dataset for deep learning algorithms," Computers, Materials & Continua, vol. 69, no.1, pp. 1183–1200, 2021.
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