Bat-Genetic Encryption Technique

Mousa, Hamdy M.

doi:10.5815/ijisa.2019.11.01

Cited by 6 publications

(7 citation statements)

References 21 publications

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“…When dealing with finding optimal solutions and stochastic search [15], then genetic algorithm is the best bet. These algorithms relies on the combination of chromosome populations, selection, crossover, and mutation [16] to produce new offspring. [17] summarizes the steps followed by GA to arrive at optimal solutions as follows:…”

Section: Genetic Algorithmmentioning

confidence: 99%

Credit Card Fraud Detection System Using Machine Learning

Makolo¹,

Adeboye²

2021

IJITCS

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The security of any system is a key factor toward its acceptability by the general public. We propose an intuitive approach to fraud detection in financial institutions using machine learning by designing a Hybrid Credit Card Fraud Detection (HCCFD) system which uses the technique of anomaly detection by applying genetic algorithm and multivariate normal distribution to identify fraudulent transactions on credit cards. An imbalance dataset of credit card transactions was used to the HCCFD and a target variable which indicates whether a transaction is deceitful or otherwise. Using F-score as performance metrics, the model was tested and it gave a prediction accuracy of 93.5%, as against artificial neural network, decision tree and support vector machine, which scored 84.2%, 80.0% and 68.5% respectively, when trained on the same data set. The results obtained showed a significant improvement as compared with the other widely used algorithms.

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Section: Genetic Algorithmmentioning

confidence: 99%

Credit Card Fraud Detection System Using Machine Learning

Makolo¹,

Adeboye²

2021

IJITCS

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“…On the other hand, a technique that uses GA along with Random Number System (RNS) for encryption and decryption of data was implemented the same year [5]. Another technique that customizes GA along with some arithmetic and logical operators to secure the data has proved to be very effective [14].…”

Section: Genetic Algorithm (Ga) In Cryptographymentioning

confidence: 99%

Improving Cloud Data Encryption Using Customized Genetic Algorithm

Arshad¹,

Umair²,

Munawar³

et al. 2020

IJISA

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Data Encryption is widely utilized for ensuring data privacy, integrity, and confidentiality. Nowadays, a large volume of data is uploaded to the cloud, which increases its vulnerability and adds to security breaches. These security breaches include circumstances where sensitive information is being exposed to third parties or any access to sensitive information by unauthorized personnel. The objective of this research is to propose a method for improving encryption by customizing the genetic algorithm (GA) with added steps of encryption. These added steps of encryption include the data being processed with local information (chromosome's value calculated with computer-generated random bits without human intervention). The improvement in the randomness of the key generated is based on altering the population size, number of generations, and mutation rate. The first step of encrypting is to convert sample data into binary form. Once the encryption process is complete, this binary result is converted back to get the encrypted data or cipher-text. Foremost, the GA operators (population size, number of generations, and mutation rate) are changed to determine the optimal values of each operator to bring forth a random key in the minimum possible time, then local intelligence is headed in the algorithm to further improve the outcomes. Local Intelligence consists of local information and a random bit generated in each iteration. Local Information is the current value of a parent in each iteration at the gene level. Both local information and random bit are then applied in a mathematical pattern to generate a randomized key. The local intelligence-based algorithm can operate better in terms of time with the same degree of randomness that is generated with the conventional GA technique. The result showed that the proposed method is at least 80% more efficient in terms of time while generating the secret key with the same randomness level as generated by a conventional GA. Therefore, when large data are intended to be encrypted, then using local intelligence can demonstrate to be better utilized time.

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“…The best solution for keeping safe data transfer is the use of cryptography as a technique for encrypting and decrypting messages soany body can not interpret it, only the message sender and the receiver can. Consequently, data encryption has become an inevitable and integral part of any application in e-commerce [4][5][6]. Therefore, it is very important to encrypt data using a robust and fast encryption algorithm in order to make sure of secret transmission and data delivery to protect it from any intruder.…”

Section: Introductionmentioning

confidence: 99%

Features of genetic algorithm for plain text encryption

Abduljabbar

Hamid

Alhyani

2021

IJECE

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The data communication has been growing in present day. Therefore, the data encryption became very essential in secured data transmission and storage and protecting data contents from intruder and unauthorized persons. In this paper, a fast technique for text encryption depending on genetic algorithm is presented. The encryption approach is achieved by the genetic operators Crossover and mutation. The encryption proposal technique based on dividing the plain text characters into pairs, and applying the crossover operation between them, followed by the mutation operation to get the encrypted text. The experimental results show that the proposal provides an important improvement in encryption rate with comparatively high-speed Processing.

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Bat-Genetic Encryption Technique

Cited by 6 publications

References 21 publications

Credit Card Fraud Detection System Using Machine Learning

Credit Card Fraud Detection System Using Machine Learning

Improving Cloud Data Encryption Using Customized Genetic Algorithm

Features of genetic algorithm for plain text encryption

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