Intelligent Model Using CGAN and RL for Efficient Contextual Dataset Generation

Recent advances in machine learning have demonstrated the effectiveness of Conditional Generative Adversarial Network (CGAN) and Reinforcement Learning (RL) techniques in a variety of domains, from image synthesis to decision-making tasks. However, their integration and application to the generation of contextual datasets remains under explored. This paper proposes a novel approach to combine CGAN and RL to increase dataset accuracy in specific context domains. The proposed methodology focuses on using CGANs to generate synthetic data that closely mimic real-world contextual variations corresponding to Advanced Persistent Threat (APT) attacks. By adapting the generator to contextual variables such as environmental conditions or attacker behavior, the generated data are more closely aligned to the target distribution, improving model robustness and generalization. In addition, RL techniques are used to iteratively refine the generated data samples, leading the generator to produce samples that not only adhere to the desired context, but also match the specific objectives of the proposed work. The main contributions of this work include the development of a unified framework that seamlessly integrates CGAN and RL for generating contextual datasets to strengthen Deep Learning Models. The proposed algorithms have been implemented using python programming language along with APIs. Experimental results demonstrate significant improvements in accuracy and reliability compared to traditional dataset augmentation methods.