Today’s networks undoubtedly require a high level of protection from cyber threats and attacks. State-of-the-art solutions that implement Machine Learning (ML) have shown to improve the accuracy and confidence in threat detection compared to previous approaches, making it suitable for the de- tection of today’s sophisticated attacks such as Distributed Denial of Service (DDoS). However, in real-world deployments, input data streams take large bandwidth and processing, especially for Deep Learning (DL) solutions that require extensive input data. The deployment environments usually have limited bandwidth and computing resources, such as for the Internet of Things (IoT). Thus, a lightweight detection solution that satisfies such constraints is needed. In this paper, we utilize a feature reduction approach for our DL-based DDoS detector using the Analysis of Variance (ANOVA), which is used to identify important data features and reduce the data inputs needed for detection. Our result shows that we can reduce the data input needed by up to 84.21% while only reducing 0.1% detection accuracy. We also provide a detailed analysis of the characteristics of DDoS attacks using ANOVA and compared our work with recent DL- based DDoS detection systems to demonstrate that our results are comparable to existing approaches.