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All Journal Advance Sustainable Science, Engineering and Technology (ASSET) Scientific Journal of Informatics
Folasade Olubusola Isinkaye
Ekiti State University
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology

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A Combination of Vigenere Cipher and Advanced Encryption Standard for Image Security Ivan Stepheng; Christy Atika Sari; Eko Hari Rachmawanto; Folasade Olubusola Isinkaye
Advance Sustainable Science Engineering and Technology Vol 5, No 3 (2023): August-October
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v5i3.17150

Abstract

In an era where digital information security is paramount, this research addresses the pressing need for robust encryption methods. We propose a novel approach that combines the Vigenere Cipher and the Advanced Encryption Standard (AES) for secure digital image transmission. Our study recognizes the research gap in secure image transmission methods and aims to bridge it with a powerful encryption solution. We implement this hybrid encryption approach using the Vigenere Cipher in C++ and the AES algorithm in MATLAB. Our experiments validate the effectiveness of our program in concealing and restoring digital images during transmission. This hybrid encryption technique has promising applications in healthcare, military, and confidential business operations, bolstering image security in real-life scenarios. By enhancing image security, our research can contributed to safeguarding sensitive information in the digital age
Performance Analysis of Support Vector Classification and Random Forest in Phishing Email Classification Chaerul Umam; Lekso Budi Handoko; Folasade Olubusola Isinkaye
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.3301

Abstract

Purpose: This study aims to conduct a performance analysis of phishing email classification system using machine learning algorithms, specifically Random Forest and Support Vector Classification (SVC). Methods/Study design/approach: The study employed a systematic approach to develop a phishing email classification system utilizing machine learning algorithms. Implementation of the system was conducted within the Jupyter Notebook IDE using the Python programming language. The dataset, sourced from kaggle.com, comprised 18,650 email samples categorized into secure and phishing emails. Prior to model training, the dataset was divided into training and testing sets using three distinct split percentages: 60:40, 70:30, and 80:20. Subsequently, parameters for both the Random Forest and Support Vector Classification models were carefully selected to optimize performance. The TF-IDF Vectorizer method was employed to convert text data into vector form, facilitating structured data processing. Result/Findings: The study's findings reveal notable performance accuracies for both the Random Forest model and Support Vector Classification across varying data split percentages. Specifically, the Support Vector Classification consistently outperforms the Random Forest model, achieving higher accuracy rates. At a 70:30 split percentage, the Support Vector Classification attains the highest accuracy of 97.52%, followed closely by 97.37% at a 60:40 split percentage. Novelty/Originality/Value: Comparisons with previous studies underscored the superiority of the Support Vector Classification model. Therefore, this research contributes novel insights into the effectiveness of this machine learning algorithms in phishing email classification, emphasizing its potential in enhancing cybersecurity measures.
Co-Authors Chaerul Umam Christy Atika Sari Eko Hari Rachmawanto Ivan Stepheng L. Budi Handoko