Building Auto-Encoder Intrusion Detection System Based on Random Forest Feature Selection

Abstract Machine learning techniques have been widely used in intrusion detection for many years. However, these techniques are still suffer from lack of labeled dataset, heavy overhead and low accuracy. To improve classification accuracy and reduce training time, this paper proposes an effective deep learning method, namely AE-IDS (Auto-Encoder Intrusion Detection System) based on random forest algorithm. This method constructs the training set with feature selection and feature grouping. After training, the model can predict the results with auto-encoder, which greatly reduces the detection time and effectively improves the prediction accuracy. The experimental results show that the proposed method is superior to traditional machine learning based intrusion detection methods in terms of easy training, strong adaptability, and high detection accuracy.