Open Access

ARTICLE

Cryptographic Based Secure Model on Dataset for Deep Learning Algorithms

Muhammad Tayyab^1,*, 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:

Computers, Materials & Continua 2021, 69(1), 1183-1200. https://doi.org/10.32604/cmc.2021.017199

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.

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Keywords

Deep learning (DL); poisoning attacks; evasion attacks; neural network; hash functions SHA512; homomorphic encryption scheme

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