Application of a Deep Learning Algorithm in Network Intrusion

In the wave of digital transformation, data is becoming increasingly important. The leakage or loss of critical data can cause huge losses to enterprises or individuals. However, in recent years, network security incidents occur frequently, and how to ensure network security has become a widespread concern. Compared to the traditional network protection model, the intrusion detection method can more accurately and effectively detect abnormal behaviors in the network. However, The traditional k-means clustering algorithm faces challenges in intrusion detection, including limited adaptability to diverse datasets and low accuracy. To further improve its performance, Based on the K-means algorithm, this article proposes an improved TS-Kmeans algorithm for use in network security intrusion detection models, and conducts comparative simulation experiments using a dataset. Practice has proven that the accuracy of the improved algorithm proposed in this article has been significantly enhanced, and the false alarm rate has also been reduced.