An Innovative Approach Using TKN-Cryptology for Identifying the Replay Assault

Various organizations store data online rather than on physical servers.As the number of user’s data stored in cloud servers increases,the attack rate to access data from cloud servers also increases.Different researchers worked on different algorithms to protect cloud data from replay attacks.None of the papers used a technique that simultaneously detects a full-message and partial-message replay attack.This study presents the development of a TKN(Text,Key and Name)cryptographic algorithm aimed at protecting data from replay attacks.The program employs distinct ways to encrypt plain text[P],a user-defined Key[K],and a Secret Code[N].The novelty of the TKN cryptographic algorithm is that the bit value of each text is linked to another value with the help of the proposed algorithm,and the length of the cipher text obtained is twice the length of the original text.In the scenario that an attacker executes a replay attack on the cloud server,engages in cryptanalysis,or manipulates any data,it will result in automated modification of all associated values inside the backend.This mechanism has the benefit of enhancing the detectability of replay attacks.Nevertheless,the attacker cannot access data not included in any of the papers,regardless of how effective the attack strategy is.At the end of paper,the proposed algorithm’s novelty will be compared with different algorithms,and it will be discussed how far the proposed algorithm is better than all other algorithms.

Hill Matrix and Radix-64 Bit Algorithm to Preserve Data Confidentiality

There are many cloud data security techniques and algorithms available that can be used to detect attacks on cloud data,but these techniques and algorithms cannot be used to protect data from an attacker.Cloud cryptography is the best way to transmit data in a secure and reliable format.Various researchers have developed various mechanisms to transfer data securely,which can convert data from readable to unreadable,but these algorithms are not sufficient to provide complete data security.Each algorithm has some data security issues.If some effective data protection techniques are used,the attacker will not be able to decipher the encrypted data,and even if the attacker tries to tamper with the data,the attacker will not have access to the original data.In this paper,various data security techniques are developed,which can be used to protect the data from attackers completely.First,a customized American Standard Code for Information Interchange(ASCII)table is developed.The value of each Index is defined in a customized ASCII table.When an attacker tries to decrypt the data,the attacker always tries to apply the predefined ASCII table on the Ciphertext,which in a way,can be helpful for the attacker to decrypt the data.After that,a radix 64-bit encryption mechanism is used,with the help of which the number of cipher data is doubled from the original data.When the number of cipher values is double the original data,the attacker tries to decrypt each value.Instead of getting the original data,the attacker gets such data that has no relation to the original data.After that,a Hill Matrix algorithm is created,with the help of which a key is generated that is used in the exact plain text for which it is created,and this Key cannot be used in any other plain text.The boundaries of each Hill text work up to that text.The techniques used in this paper are compared with those used in various papers and discussed that how far the current algorithm is better than all other algorithms.Then,the Kasiski test is used to verify the validity of the proposed algorithm and found that,if the proposed algorithm is used for data encryption,so an attacker cannot break the proposed algorithm security using any technique or algorithm.

An Efficient Technique to Prevent Data Misuse with Matrix Cipher Encryption Algorithms

Many symmetric and asymmetric encryption algorithms have been developed in cloud computing to transmit data in a secure form.Cloud cryptography is a data encryption mechanism that consists of different steps and prevents the attacker from misusing the data.This paper has developed an efficient algorithm to protect the data from invaders and secure the data from misuse.If this algorithm is applied to the cloud network,the attacker will not be able to access the data.To encrypt the data,the values of the bytes have been obtained by converting the plain text to ASCII.A key has been generated using the Non-Deterministic Bit Generator(NRBG)mechanism,and the key is XNORed with plain text bits,and then Bit toggling has been implemented.After that,an efficient matrix cipher encryption algorithm has been developed,and this algorithm has been applied to this text.The capability of this algorithm is that with its help,a key has been obtained from the plain text,and only by using this key can the data be decrypted in the first steps.A plain text key will never be used for another plain text.The data has been secured by implementing different mechanisms in both stages,and after that,a ciphertext has been obtained.At the end of the article,the latest technique will be compared with different techniques.There will be a discussion on how the present technique is better than all the other techniques;then,the conclusion will be drawn based on comparative analysis.

Sailfish Optimizer with EfficientNet Model for Apple Leaf Disease Detection

Recent developments in digital cameras and electronic gadgets coupled with Machine Learning(ML)and Deep Learning(DL)-based automated apple leaf disease detection models are commonly employed as reasonable alternatives to traditional visual inspection models.In this background,the current paper devises an Effective Sailfish Optimizer with EfficientNet-based Apple Leaf disease detection(ESFO-EALD)model.The goal of the proposed ESFO-EALD technique is to identify the occurrence of plant leaf diseases automatically.In this scenario,Median Filtering(MF)approach is utilized to boost the quality of apple plant leaf images.Moreover,SFO with Kapur’s entropy-based segmentation technique is also utilized for the identification of the affected plant region from test image.Furthermore,Adam optimizer with EfficientNet-based feature extraction and Spiking Neural Network(SNN)-based classification are employed to detect and classify the apple plant leaf images.A wide range of simulations was conducted to ensure the effective outcomes of ESFO-EALD technique on benchmark dataset.The results reported the supremacy of the proposed ESFO-EALD approach than the existing approaches.

An Optimization Approach for Convolutional Neural Network Using Non-Dominated Sorted Genetic Algorithm-Ⅱ

In computer vision,convolutional neural networks have a wide range of uses.Images representmost of today’s data,so it’s important to know how to handle these large amounts of data efficiently.Convolutional neural networks have been shown to solve image processing problems effectively.However,when designing the network structure for a particular problem,you need to adjust the hyperparameters for higher accuracy.This technique is time consuming and requires a lot of work and domain knowledge.Designing a convolutional neural network architecture is a classic NP-hard optimization challenge.On the other hand,different datasets require different combinations of models or hyperparameters,which can be time consuming and inconvenient.Various approaches have been proposed to overcome this problem,such as grid search limited to low-dimensional space and queuing by random selection.To address this issue,we propose an evolutionary algorithm-based approach that dynamically enhances the structure of Convolution Neural Networks(CNNs)using optimized hyperparameters.This study proposes a method using Non-dominated sorted genetic algorithms(NSGA)to improve the hyperparameters of the CNN model.In addition,different types and parameter ranges of existing genetic algorithms are used.Acomparative study was conducted with various state-of-the-art methodologies and algorithms.Experiments have shown that our proposed approach is superior to previous methods in terms of classification accuracy,and the results are published in modern computing literature.