Boosting Intrusion Detection Accuracy With a Multibranch Deep Learning Framework

To overcome the limitations of traditional intrusion detection methods in dealing with high‐dimensional sparse features, multiclass attack classification, and model robustness, this paper presents a fused multibranch intrusion detection model (FMB‐IDM). The proposed framework combines three complementary deep learning components. First, an MLP branch equipped with a Squeeze‐and‐Excitation attention module is used to enhance feature representation by adaptively reweighting channel‐wise information. Second, a TabTransformer‐based branch is introduced to model complex dependencies among structured input features through self‐attention. Third, a CNN‐BiLSTM branch is employed to capture both local feature patterns and longer‐range sequential relationships, where the CNN extracts local representations, and the two‐layer bidirectional LSTM learns contextual dependencies. To make better use of the information learned by each branch, an attention‐based fusion strategy is adopted to combine their outputs adaptively, which improves the model’s ability to distinguish different types of intrusion behaviors. Experiments conducted on the NSL‐KDD, UNSW‐NB15, and CIC‐IDS2017 datasets demonstrate that the proposed model achieves strong and consistent performance in both binary and multiclass intrusion detection tasks. In particular, on CIC‐IDS2017, FMB‐IDM reaches an accuracy of 97.56% and a weighted F1‐score of 0.98. In addition, the model maintains good inference efficiency, with an average latency of about 0.03 ms per sample under the experimental hardware configuration.