<p>Distributed Denial of Service (DDoS) attacks have arisen as one of the most critical challenges in network security, requiring advanced detection methods that are both accurate and efficient. This paper introduces the Quantized Convolutional Accelerated Detection (Q-CAD) model, a novel approach designed to address these challenges by significantly enhancing detection speed and computational efficiency. The Q-CAD model operates on data preprocessed via the Allocated Time-Slot Packet Aggregation (ATSPA) method, which improves detection efficiency by aggregating network packets into time slots for faster and more precise identification of anomalous traffic patterns. Additionally, the model applies quantization techniques, including Ternary Neural Networks (TNN), Ternary-Binary Networks (TBN), and Binary Neural Networks (BNN), to drastically reduce the computational complexity and model size without sacrificing accuracy. To further optimize performance, the Channel Concatenation-Based Quantized Inference (CCQI) technique is employed, which results in a processing speedup of up to 40 times compared to non-quantized models. The experimental results, using both the CICDDoS2017 and Bot-IoT datasets, demonstrate that Q-CAD consistently achieves high accuracy while significantly reducing inference latency, making it highly suitable for real-time deployment on edge devices. This research contributes a robust and scalable solution to DDoS detection, addressing the critical need for efficient, low-latency defense mechanisms in modern network environments.</p>