The field of generative artificial intelligence (AI) is experiencing rapid advancements, impacting a multitude of sectors, from computer vision to healthcare. This paper provides a comprehensive review of generative AI’s evolution, significance, and applications, including the foundational architectures such as generative adversarial networks (GANs), variational autoencoders (VAEs), autoregressive models, flow‐based models, and diffusion models. We delve into the impact of generative algorithms on computer vision, natural language processing, artistic creation, and healthcare, demonstrating their revolutionary potential in data augmentation, text and speech synthesis, and medical image interpretation. While the transformative capabilities of generative AI are acknowledged, the paper also examines ethical concerns, most notably the advent of deepfakes, calling for the development of robust detection frameworks and responsible use guidelines. As generative AI continues to evolve, driven by advances in neural network architectures and deep learning methodologies, this paper provides a holistic overview of the current landscape and a roadmap for future research and ethical considerations in generative AI.

Cloud computing is a vast revolution in information technology (IT) that inhibits scalable and virtualized sources to end users with low infrastructure cost and maintenance. They also have much flexibility and these resources are supervised by various management organizations and provided over the Internet by known standards, formats, and networking protocols. Legacy protocols and underlying technologies consist of vulnerabilities and bugs which open doors for intrusion by network attackers. Attacks as distributed denial of service (DDoS) are one of most frequent attacks, which impose heavy damage and affect performance of the cloud. In this research work, DDoS attack detection is easily identified in an optimized way through a novel algorithm, namely, the proposed gradient hybrid leader optimization (GHLBO) algorithm. This optimized algorithm is responsible to train a deep stacked autoencoder (DSA) that detects the attack in an efficient manner. Here, fusion of features is carried out by deep maxout network (DMN) with an overlap coefficient, and augmentation of data is carried out by the oversampling process. Furthermore, the proposed GHLBO is generated by integrating the gradient descent and hybrid leader‐based optimization (HLBO) algorithm. Also, this proposed method is assessed by various performance metrics, such as the true positive rate (TPR), true negative rate (TNR), and testing accuracy with values attained as 0.909, 0.909, and 0.917, accordingly.