<p>In the realm of cybersecurity, distributed denial-of-services (DDoS) attacks present a significant and severe threat to computer networks. Particularly targeting governmental entities, these attacks hold paramount significance on national security agendas worldwide. With the digital landscape's expansion and its assimilation of cloud technologies, the internet has transformed into a battlefield for disruptive DDoS attacks, causing extensive disruptions. Designing a robust defense strategy is challenging because of the diverse attack patterns, heterogeneous communication protocols, and evolving tactics. This paper will delve into a comprehensive exploration of diverse methodologies of deep learning (DL) approaches to address the task of detecting DDoS attacks. This will be accomplished through an extensive review of recent research contributions in the realm of DDoS attack detection using DL. The paper reviews contributions, analyses and provides insights on benchmarked datasets, and discusses employed evaluation metrics. Additionally, it distils key findings, addresses challenges inherent in this domain comprehensively, and delves into future research prospects. Serving as a pivotal resource, the paper facilitates understanding, evaluation, and the trajectory of DDoS attack detection. The findings of this research hold the promise of yielding valuable insights that can lead to the development of highly efficient solutions for DDoS detection, based on the principles of DL.</p>