The paper explores the use of deep learning techniques to detect and mitigate botnet attacks in Software-Defined Networks (SDNs), emphasizing the challenges and opportunities in this emerging architecture. It presents a new dataset and highlights the performance of CNN methods, achieving detection rates of 99% for normal flows and 97% for attack flows. The study aims to develop a lightweight deep learning method with baseline hyper-parameters suitable for detecting such threats efficiently.