Network intrusion detection systems play an important role in protecting the network from attacks. However, Existing network intrusion data is imbalanced, which makes it difficult to accurately detect minority attacks, and the training and detection time of deep neural network detection systems is relatively long. According to these problems, this paper proposes a network intrusion detection system based on adaptive synthetic (ADASYN) oversampling technology and LightGBM. First, we normalize and one-hot encode the original data through data preprocessing to avoid the impact of the maximum or minimum value on the overall characteristics. Second, we increase the minority samples by ADASYN oversampling technology to solve the problem of the low detection rate of minority attacks due to the imbalance of the training data. Finally, the LightGBM ensemble learning model is used to further reduce the time complexity of the system while ensuring the accuracy of detection. Through experimental verification on the NSL-KDD, UNSW-NB15 and CICIDS2017 data sets, the results show that the detection rate of minority samples can be improved after ADASYN oversampling, thereby improving the overall accuracy rate. The accuracy of the proposed algorithm is up to 92.57%, 89.56% and 99.91% respectively in the three test sets, and it consumes less time in the training and detection process, which is superior to other existing methods.

网络入侵检测系统在保护网络免受攻击方面起着重要作用。但是，现有的网络入侵数据是不平衡的，这使得难以准确检测少数攻击，并且深度神经网络检测系统的训练和检测时间相对较长。针对这些问题，本文提出了一种基于自适应合成（ADASYN）过采样技术和LightGBM的网络入侵检测系统。首先，我们通过数据预处理对原始数据进行规范化和一次性编码，以避免最大值或最小值对整体特征的影响。其次，我们通过ADASYN过采样技术增加了少数样本，以解决训练数据不均衡导致少数攻击检测率低的问题。最后，LightGBM集成学习模型用于进一步降低系统的时间复杂度，同时确保检测的准确性。通过对NSL-KDD，UNSW-NB15和CICIDS2017数据集的实验验证，结果表明ADASYN过采样后可以提高少数样品的检出率，从而提高整体准确率。该算法在三个测试集中的准确率分别达到92.57％，89.56％和99.91％，在训练和检测过程中消耗的时间更少，优于其他现有方法。结果表明，ADASYN过采样后可以提高少数样品的检出率，从而提高整体准确率。该算法在三个测试集中的准确率分别达到92.57％，89.56％和99.91％，在训练和检测过程中消耗的时间更少，优于其他现有方法。结果表明，ADASYN过采样后可以提高少数样品的检出率，从而提高整体准确率。该算法在三个测试集中的准确率分别达到92.57％，89.56％和99.91％，在训练和检测过程中消耗的时间更少，优于其他现有方法。