Network Intrusion Detection System (NIDS) is one of the key technologies to prevent network attacks and data leakage. In combination with machine learning, intrusion detection has achieved great progress in recent years. However, due to the diversity of intrusion types, the representation learning ability of the existing models is still deficient, which limits the further improvement of the detection performance. Meanwhile, with the increasing of model complexity, the training time becomes longer and longer. In this paper, we propose a Dynamic Deep Forest method for network intrusion detection. It uses cascade tree structure to strengthen the representation learning ability. At the same time, the training process is accelerated due to small-scale parameter fitting and dynamic level-growing strategy. The proposed Dynamic Deep Forest is a tree-based ensemble approach and consists of two parts. The first part, Multi-Grained Traversing, uses selectors to pick up features as complete as possible. The selectors are constructed dynamically so that the training process will stop as soon as the optimal feature combination is found. The second part, Cascade Forest, introduces level-by-level tree structures. It has fewer hyper-parameters and follows a dynamic level-growing strategy to reduce model complexity. In experiments, we evaluate our model on network intrusion dataset KDD’99. The results show that the Dynamic Deep Forest method obtains higher recall and precision through a short time of model training. Moreover, the Dynamic Deep Forest method has lower risk of misclassification, which is more stable and reliable in a real network environment.
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