چکیده مقاله
The rise of quantum computing has brought about a significant threat to the security of current asymmetriccryptography methods To address this issue, neural cryptography has emerged as a potential alternative that islightweight and efficient in resisting known quantum computer algorithms As the implementation of quantumcomputing could expose IoT sensors and smart grid systems to a range of attack vectors, the need for secure andefficient cryptography solutions is crucial This paper explores the effectiveness of using integer valued input vectorsto enhance the synchronization of the Tree Parity Machine, which is a type of neural cryptography The introductionof a new parameter M, which indicates the minimum and maximum values of input vector elements, plays a key rolein evaluating the nonbinary version of the mutual learning algorithm in a simulated insecure environment The findingssuggest that while there may be some trade offs between security and synchronization time with the Nonbinary TreeParity Machine, the speed improvement resulting from the enhancement outweighs the decrease in security Thisenhancement is particularly impactful for smaller adjustments to the parameter M, highlighting the potential of neuralcryptography for securing IoT sensors and smart grid systems
کلیدواژهها
نویسندگان
Morteza Ghorbani
dept engineering, school of mechanical Engineering Islamic Azad University of Mashhad۱Mashhad, Iran
Mahdi Afshar
Department of Electrical Engineering, Ragheb Isfahani Higher Education Institute, Isfahan, Iran
Alireza Norouzpour Shahrbejari
dept Electrical Engineering TUVTehran, Iran
شیوه ارجاع
Ghorbani, Morteza and Afshar, Mahdi and Norouzpour Shahrbejari, Alireza,1402,Security of Neural Network-Based Protocolfor Smart Grids,The 15th International Conference on Innovation and Research in Engineering Sciences
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