Software anomaly detection technology based on deep learning

In recent years, with the widespread popularity of software applications, software security issues have become increasingly prominent. Abnormal software operation is one of the main causes of software security issues, and the application of artificial intelligence algorithms provides new ideas for software anomaly detection. This article proposes a software anomaly detection technique based on deep learning to address common software runtime issues. Firstly, a software anomaly detection model based on deep learning was constructed. Secondly, the effectiveness of the model was tested on the Kubernetes platform. The experimental results show that the accuracy of the software anomaly detection method based on deep learning proposed in this paper reaches 98.98%, which can achieve efficient detection of software anomalies.