Cryptographic Based Secure Model on Dataset for Deep Learning Algorithms

作     者：Muhammad Tayyab Mohsen Marjani N.Z.Jhanjhi Ibrahim Abaker Targio Hashim Abdulwahab Ali Almazroi Abdulaleem Ali Almazroi 

作者机构：School of Computer Science and Engineering(SCE)Taylor’s University Lake-Side CampusSubang Jaya47500Malaysia Department of Computer ScienceCollege of Computing and InformaticsUniversity of SharjahSharjah27272UAE University of JeddahCollege of Computing and Information Technology at KhulaisDepartment of Information TechnologyJeddahSaudi Arabia Department of Computer ScienceRafha Community CollegeNorthern Border UniversityArar91431Saudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第69卷第10期

页      面：1183-1200页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0801[工学-力学（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Baylor University 

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

摘      要：Deep learning(DL)algorithms have been widely used in various security applications to enhance the performances of decision-based *** data added by an attacker can cause several security and privacy problems in the operation of DL *** two most common active attacks are poisoning and evasion attacks,which can cause various problems,including wrong prediction and misclassification of decision-based ***,to design an efficient DL model,it is crucial to mitigate these *** this regard,this study proposes a secure neural network(NN)model that provides data security during model training and testing *** main idea is to use cryptographic functions,such as hash function(SHA512)and homomorphic encryption(HE)scheme,to provide authenticity,integrity,and confidentiality of *** performance of the proposed model is evaluated by experiments based on accuracy,precision,attack detection rate(ADR),and computational *** 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 ***,the proposed model can be used to detect attacks and mitigate the attacker *** results also show that the computational cost of the proposed model does not increase with model complexity.