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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Abdullah, Ezmin
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Hybrid encryption based on a generative adversarial network Amir, Iqbal; Suhaimi, Hamizan; Mohamad, Roslina; Abdullah, Ezmin; Pu, Chuan-Hsian
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp971-978

Abstract

In today’s world, encryption is crucial for protecting sensitive data. Neural networks can provide security against adversarial attacks, but meticulous training and vulnerability analysis are required to ensure their effectiveness. Hence, this research explores hybrid encryption based on a generative adversarial network (GAN) for improved message encryption. A neural network was trained using the GAN method to defend against adversarial attacks. Various GAN training parameters were tested to identify the best model system, and various models were evaluated concerning their accuracy against different configurations. Neural network models were developed for Alice, Bob, and Eve using random datasets and encryption. The models were trained adversarially using the GAN to find optimal parameters, and their performance was analyzed by studying Bob’s and Eve’s accuracy and bits error. The parameters of 8,000 epochs, a batch size of 4,096, and a learning rate of 0.0008 resulted in 100% accuracy for Bob and 52.14% accuracy for Eve. This implies that Alice and Bob’s neural network effectively secured the messages from Eve’s neural network. The findings highlight the advantages of employing neural network-based encryption methods, providing valuable insights for advancing the field of secure communication and data protection.
A simulation-based investigation into the bidirectional charge and discharge dynamics in lead-acid batteries Noor Zelan, Muhammad Aiman; Hidayat, Muhammad Nabil; Nik Ali, Nik Hakimi; Umair, Muhammad; Mohd Mawardi, Muhammad Izzul; Ahmad, Ahmad Sukri; Abdullah, Ezmin
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp783-796

Abstract

This paper presents a comprehensive simulation-based investigation into the bidirectional charge and discharge dynamics of lead-acid batteries within electric vehicles (EVs) and energy storage systems (ESS). Utilizing a bidirectional DC-DC converter (BDC) integrated with a lead-acid battery, the study explores the performance of these batteries through various charging and discharging scenarios. The simulation model, implemented using MATLAB, assesses the impact of charging strategies on battery behavior, focusing on key metrics such as state of charge (SOC), energy performance, and charging rates. The results reveal that lead-acid batteries, when paired with appropriate charging infrastructure and strategies, demonstrate enhanced performance and reliability in both EV and ESS applications. The study highlights the significant role of BDC topology in facilitating efficient energy transfer and optimizing battery usage. The findings underscore the potential for improved performance and widespread adoption of bidirectional converters in sustainable energy solution.
Co-Authors Ahmad, Ahmad Sukri Amir, Iqbal Hidayat, Muhammad Nabil Mohamad, Roslina Mohd Mawardi, Muhammad Izzul Nik Ali, Nik Hakimi Noor Zelan, Muhammad Aiman Pu, Chuan-Hsian Suhaimi, Hamizan Umair, Muhammad