Many Distributed Denial of Service （DDoS） attack detection methods focus on improving model performance， but ignore the influence of traffic sample distribution and feature dimension on detection performance， resulting in the model learning redundant information. To address the problems of network traffic class imbalance and feature redundancy， a Hybrid Feature Selection method based on Multiple Evaluation Criteria （HFS-MEC） was proposed. Firstly， the Pearson Correlation Coefficient （PCC） and Mutual Information （MI） were considered comprehensively to select the correlation features. Then， the Sequential Backward Selection （SBS） algorithm based on Variance Inflation Factor （VIF） was designed to reduce the feature redundancy and further reduce the feature dimension. At the same time， to balance the detection performance and computation time， a Low-latency DDoS attack detection model based on Simple Recurrent Unit （SRU） （L-DDoS-SRU） was designed. Experiments were carried out on the CICIDS2017 and CICDDoS2019 datasets. The results show that HFS-MEC reduces the feature dimensions from 78 and 88 to 31 and 41， respectively； on the CICDDoS2019 dataset， L-DDoS-SRU reduces the detection time to only 40.34 seconds with a recall of 99.38%， which is improved by 8.47% compared to that of Long Short-Term Memory （LSTM）， and is increased by 9.76% compared to that of Gated Recurrent Unit （GRU）. The above verifies that the proposed method improves the detection performance and reduces the detection time effectively.
