Unimodular matrix and bernoulli map on text encryption algorithm using python

Arifin, Samsul; Muktyas, Indra Bayu; Prasetyo, Puguh Wahyu; Abdillah, Abdul Azis

doi:10.24042/ajpm.v12i2.10469

Cited by 5 publications

(8 citation statements)

References 11 publications

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“…It categorizes Indonesian tweets into three categories: no hate speech, abusive but not hate speech, and abusive and hate speech. However, like previous studies on hate speech and abusive language identification, it does not explain why a tweet is deemed a hateful message [14].…”

Section: Introductioncontrasting

confidence: 58%

An Explainable AI Model for Hate Speech Detection on Indonesian Twitter

Ibrahim

Arifin

Yudistira

et al. 2022

CommIT (Communication and Information Technology) Journal

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To avoid citizen disputes, hate speech on social media, such as Twitter, must be automatically detected. The current research in Indonesian Twitter focuses on developing better hate speech detection models. However, there is limited study on the explainability aspects of hate speech detection. The research aims to explain issues that previous researchers have not detailed and attempt to answer the shortcomings of previous researchers. There are 13,169 tweets in the dataset with labels like “hate speech” and “abusive language”. The dataset also provides binary labels on whether hate speech is directed to individual, group, religion, race, physical disability, and gender. In the research, classification is performed by using traditional machine learning models, and the predictions are evaluated using an Explainable AI model, such as Local Interpretable Model-Agnostic Explanations (LIME), to allow users to comprehend why a tweet is regarded as a hateful message. Moreover, models that perform well in classification perceive incorrect words as contributing to hate speech. As a result, such models are unsuitable for deployment in the real world. In the investigation, the combination of XGBoost and logical LIME explanations produces the most logical results. The use of the Explainable AI model highlights the importance of choosing the ideal model while maintaining users’ trust in the deployed model.

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Section: Introductioncontrasting

confidence: 58%

An Explainable AI Model for Hate Speech Detection on Indonesian Twitter

Ibrahim

Arifin

Yudistira

et al. 2022

CommIT (Communication and Information Technology) Journal

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“…The following is a simple example of the process of encrypting and decrypting audio files with mp3 format and assumed named B.mp3. Briefly, the file will be converted into a matrix and will be multiplied by a key matrix (unimodular) during encryption and will be multiplied by the inverse of the key matrix during decryption [7], [31], [32].…”

Section: Figure 2 Illustration Of Research Methodsmentioning

confidence: 99%

“…The following Figure 5 is a glimpse of the created program, which loads four algorithm main, namely Algorithm 1 for generating logistic map sequences, Algorithm 2 for generating a unimodular matrix (Key), Algorithm 3 for the Encryption process, and Algorithm 4 for Decryption process[1], [7]. A more complete program code can be seen at the following link: https://colab.research.google.com/drive/10a3WBsLhGIE 2XbH8vd9UbwtdEA98LwNy?usp=sharing.…”

Section: Device Name Desktop-bubm123 Processormentioning

confidence: 99%

“…c) How is the performance of this audio encryption algorithm compared to other audio encryption methods? [7] The purpose of this research is to develop a more secure and effective audio encryption algorithm. The benefit of this research is: Provides solutions to maintain audio data security, provide alternative audio encryption method that is more effective and efficient, and improves understanding of unimodular matrices and Bernoulli functions on audio data encryption.…”

Section: Introductionmentioning

confidence: 99%

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The Audio Encryption Approach uses a Unimodular Matrix and a Logistic Function

Arifin¹,

Tan²,

Ariani³

et al. 2023

IJETAE

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The encryption algorithm is very important to guard the confidentiality of audio information. One of the most effective audio encryption algorithms is to use a unimodular matrix and Bernoulli function. In this research, we develop an audio encryption algorithm that uses a unimodular matrix and a Bernoulli function. This algorithm is implemented using the language Python programming. The audio data encrypted using this algorithm is capable produce audio data that is secure and difficult for unauthorized parties to decipher authorized. In addition, this algorithm also has good performance in terms of speed and efficiency.

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“…In addition, we will also perform statistical analysis and execution time measurements to test the encryption strength of these two methods. It is hoped that this research can provide a better understanding of the application of Unimodular Hill Cipher and RSA in text encryption using Python (Zhang, 2020;Arifin et al, 2021a).…”

Section: Introductionmentioning

confidence: 99%

Application of Unimodular Hill Cipher and RSA Methods to Text Encryption Algorithms Using Python

Arifin,

Wijonarko,

Suwarno

et al. 2024

Journal of Computer Science

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Text encryption is one of the techniques used to maintain the confidentiality of information in digital communications. In this study, we propose to apply a combination of the Unimodular Hill Cipher and RSA methods to a text encryption algorithm using the Python programming language. The Unimodular Hill Cipher method uses an unimodular matrix to replace text characters with encrypted characters, while RSA (Rivest-Shamir-Adleman) is a public key encryption algorithm that relies on modulo arithmetic properties. The purpose of this research is to combine the strengths of the two methods and produce a more secure text encryption system. Unimodular Hill Cipher provides the advantage of randomizing text characters by using matrix modulo operations, while RSA provides a high level of security through the use of public and private key pairs. In this study, we explain in detail the basic theory and algorithms of the Unimodular Hill Cipher and RSA. We also describe the implementation steps of both methods in the Python programming language. The text data used in this study went through a preprocessing stage before being encrypted. We also analyze the results of the encryption using several statistical methods to measure how close the relationship between the original text and the result of the encryption is. In a comparative analysis with the previous paper, in this study, the use of the Unimodular Hill Cipher and RSA methods in the context of Python provides additional insight into the performance and level of security of both. The experimental results show that the combination of the Unimodular Hill Cipher and RSA methods can produce a higher level of security in text encryption. It is hoped that this research can contribute to the development of a more effective and secure text encryption algorithm.

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Unimodular matrix and bernoulli map on text encryption algorithm using python

Cited by 5 publications

References 11 publications

An Explainable AI Model for Hate Speech Detection on Indonesian Twitter

An Explainable AI Model for Hate Speech Detection on Indonesian Twitter

The Audio Encryption Approach uses a Unimodular Matrix and a Logistic Function

Application of Unimodular Hill Cipher and RSA Methods to Text Encryption Algorithms Using Python

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