Network intrusion detection system can effectively detect network attack behaviour, which is very important to network security. In this paper, a multiclassification network intrusion detection model based on convolutional neural network is proposed, and the algorithm is optimized. First, the data is preprocessed, the original one-dimensional network intrusion data is converted into two-dimensional data, and then the effective features are learned using optimized convolutional neural networks, and, finally, the final test results are produced in conjunction with the Softmax classifier. In this paper, KDD-CUP 99 and NSL-KDD standard network intrusion detection dataset were used to carry out the multiclassification network intrusion detection experiment; the experimental results show that the multiclassification network intrusion detection model proposed in this paper improves the accuracy and check rate, reduces the false positive rate, and also obtains better test results for the detection of unknown attacks.

中文翻译：

---

CNID：基于卷积神经网络的网络入侵检测研究

网络入侵检测系统可以有效地检测网络攻击行为，这对于网络安全非常重要。提出了一种基于卷积神经网络的多分类网络入侵检测模型，并对算法进行了优化。首先，对数据进行预处理，将原始的一维网络入侵数据转换为二维数据，然后使用优化的卷积神经网络学习有效特征，最后，结合最终的测试结果得出最终的测试结果。 Softmax分类器。本文采用KDD-CUP 99和NSL-KDD标准网络入侵检测数据集进行了多分类网络入侵检测实验。