Implementation of hashed cryptography algorithm based on cryptography message syntax

Ali, Mohammed Ahnaf (2019) Implementation of hashed cryptography algorithm based on cryptography message syntax. Masters thesis, Universiti Tun Hussein Onn Malaysia.


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This design and simulation research is conducted in CMC network security (message encryption context). The design will use MATLAB encryption to show simulation. The coding is designed in such a way that there is a malicious attack to destroy the data. The system will automatically protect data and thus retrieve data at the end of the system. The important study in this research is an automated learning system for deep learning to enhance security. Through system training, security can be improved. The incoming data will be checked and the system will determine whether it contains errors or false data. The system will determine after the defragmentation function, and the next thing is the key length tracking message programming. This is to make sure that the master message follows formatting in the hash. Anything that does not follow the hash format in the system or panel will be ignored. There is a transmitter transmitting the message in a series of blocks. There is a receiver receiving the message in a series of blocks. Messages in the transmitter are protected by fragmentation and arranged in encryption. This is according to the syntax algorithm. One of the two messages is deliberately rearranging and attacking by malicious. The problematic message uses light blue representation in simulation. The sent message uses the red color representation in the simulation. Hence, the fragmented CMS encryption algorithm will solve this problem and the errors in the message will be removed. The receiver must receive a clean message chain without errors. The requested messages sent from the sender and the receiver are used by the green receiver to represent. By the end of the research, the animation and animation system will be introduced to show the basic process of network enhancement with the automated learning system.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Electrical and Electronic Engineering > Department of Electrical Engineering
Depositing User: Mrs. Sabarina Che Mat
Date Deposited: 05 Aug 2021 03:26
Last Modified: 05 Aug 2021 03:26

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