FCNN Model for Diagnosis and Analysis of Symmetric Key Cryptosystem

Abstract

An important part of a cryptosystem is a cryptographic algorithm, which protects unauthorized attackers from obtaining private and sensitive data. This study is a research project on identifying cryptographic algorithms using deep learning techniques and categorizing cryptographic algorithms based on feature extraction. The research involves employing block cipher modes called electronic codebook with the encryption algorithms Blowfish and advanced encryption standard (AES), where the data will be encrypted using the same key and a different key. The model has been developed by changing the structure and parameters of the proposed model and the training rate of the data. This model will build several dense FCNN of n layers on regular fully connected neural networks. Its construction will consist of five hidden layers, with each layer consisting of 128 neurons and hidden layers activation Relu except for the output layer, which consists of two classifiers and the SoftMax activation function. FCNN is better able to classify big data. It is also more efficient in use, reducing complexity, with the ability to store training data. First, the fully connected neural network (FCNN) model was used to evaluate the categorization of the models. Then, all models, even the encryption forms, were evaluated utilizing true positive measurements for satisfactory classification of the identified encryption method and false positive measurements for incorrect classification. The effectiveness of the model was then calculated using the precision value, recall, loss, accuracy range, and F1-Score metrics using a confusion matrix. The FCNN model parameters will be changed to more effectively identify the encryption algorithm. In the proposed method, when using the same key, the accuracy was 81%, and when using a different key, the accuracy was 49%. The FCNN model’s adjusted weights and learning will be based on large data to define and assess encryption algorithms more effectively and efficiently