Design of Network Intrusion Detection Model Based on TCA

The traditional machine learning model cannot effectively identify the new network traffic data set, resulting in the model failure. Therefore, in this paper, by analyzing the problems of current network intrusion detection (NID) and combining the application of transfer theory in the detection model, a NID model based on transfer component analysis (TCA) is proposed. Among them, the specific mathematical derivation of the algorithm and the detection process of transfer model are introduced in detail. Then, the classification performance of KNN and SVM based on TCA algorithm for network abnormal traffic is compared. The results show that the TCA algorithm proposed in this paper can effectively improve the accuracy of NID, which is meaningful to expand the application scope of network abnormal traffic detection scheme based on machine learning.