针对传统机器学习算法在入侵检测实际应用中训练样本和测试样本分布不一致的情况下检测精准性低的问题， 提出一种基于聚类分析与迁移学习的入侵检测方法。该方法首先通过基于聚类的层次抽样技术来获取用于迁移分类训练的 少量有标记数据，使得迁移分类的数据分布尽可能和所要检测的数据分布相似，然后将基于实例的简单迁移分类模型应用于 入侵检测领域。在NSL-KDD数据集的实验结果表明，该方法相较于传统的机器学习算法，有更高的检测精准率。

When the distribution of training and test samples is inconsistent, the main problem for the practical application of traditional machine learning algorithms in intrusion detection is that the detection accuracy is low. To solve the problem, this paper proposes an instruction detection method based on clustering analysis and transfer learning. Firstly, the hierarchical sampling technology based on clustering is used to obtain a small amount of labeled data for transfer classification training, so that the distribution of data for transfer classification is as similar as possible to the data distribution to be detected. Then the simple transfer classification model is applied to the field of intrusion detection. The experimental results on the NSL-KDD data set show that the detection method has higher detection accuracy than the traditional machine learning algorithms