With the continuous evolution of the network environment and the increasing diversity of security threats, traditional intrusion detection technologies often struggle to meet the requirements of efficient and accurate identification when confronted with extremely unbalanced data samples and complex attack behaviors. In response to this issue, this paper focuses on the key challenges of network security intrusion detection and conducts a systematic study from two aspects: data augmentation and algorithm improvement. Firstly, the training bias caused by uneven category distribution is effectively alleviated through sample expansion technology. Combined with the loss function with the ability of dynamic weight adjustment, the model's ability to distinguish different attack types is improved. In terms of model design, this paper constructs a deep neural network structure, strengthens the in-depth mining and expression of attack features, and significantly improves the detection sensitivity of rare intrusion behaviors. To overcome the local optimum problem caused by the parameter initialization of deep models, an intelligent optimization algorithm based on quantum coding is introduced in the study. The initial weights of the network are optimized by simulating the evolutionary mechanism, thereby enhancing the stability and convergence speed of model training. The experimental results show that the proposed algorithm outperforms the traditional methods in multiple performance indicators, especially excelling in the identification of small sample attacks and the overall detection accuracy, verifying its practical application value and promotion potential in the field of network security defense.

Wenjie Jiang, Ziting Mai. Research on Optimization of Network Security Intrusion Detection Algorithm Based on Neural Network. Machine Learning Theory and Practice (2025), Vol. 5, Issue 1: 1-12. https://doi.org/10.38007/ML.2025.050101.

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