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Suhaimi, Hamizan
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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.
Co-Authors Abdullah, Ezmin Amir, Iqbal Mohamad, Roslina Pu, Chuan-Hsian