Spiking Neural Network (SNN) use spiking neurons that transmit information through discrete spikes, similar to the way biological neurons communicate through action potentials. This unique property of SNNs makes them suitable for applications that require real-time processing and low power consumption. This paper proposes a new method for detecting DDoS attacks using a spiking neural network (SNN) with a distance-based rate coding mechanism and optimizing the SNN using a genetic algorithm (GA). The proposed GA-SNN approach achieved a remarkable accuracy rate of 99.98% in detecting DDoS attacks, outperforming existing state-of-the-art methods. The GA optimization approach helps to overcome the challenges of setting the initial weights and biases in the SNN, and the distance-based rate coding mechanism enhances the accuracy of the SNN in detecting DDoS attacks. Additionally, the proposed approach is designed to be computationally efficient, which is essential for practical implementation in real-time systems. Overall, the proposed GA-SNN approach is a promising solution for accurate and efficient detection of DDoS attacks in network security applications.