Developing Robust Detection Systems for Deepfake Media Using Advanced AI and Data Analytics Techniques to Enhance Cyber Security

Abstract: To improve cybersecurity, this study looks into the creation of a reliable deepfake media detection system employing cutting-edge AI and data analytics approaches. A hybrid detection approach is presented by combining Recurrent Neural Networks (RNNs) for temporal analysis, Generative Adversarial Networks (GANs) for adversarial training, and Convolutional Neural Networks (CNNs) for feature extraction. Twenty professionals in the fields of cybersecurity, data analytics, and artificial intelligence were surveyed; the results were analysed using the Relative Importance Index (RII). The results underscore the hybrid model's efficacy, expandability, versatility, and pragmatic nature, stressing its capacity for instantaneous processing and assimilation into current cybersecurity structures. By offering a thorough strategy to counter the growing danger of deepfake media, this study seeks to improve the security and dependability of digital environments.

Keywords: Deepfake Detection, Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs) and Cybersecurity