A Deep Random Forest Model on Spark for Network Intrusion Detection
Zhenpeng Liu, Nan Su, Yiwen Qin, Jiahuan Lu, Xiaofei Li
Abstract
This paper focuses on an important research problem of cyberspace security. As an active defense technology, intrusion detection plays an important role in the field of network security. Traditional intrusion detection technologies have problems such as low accuracy, low detection efficiency, and time consuming. The shallow structure of machine learning has been unable to respond in time. To solve these problems, the deep learning-based method has been studied to improve intrusion detection. The advantage of deep learning is that it has a strong learning ability for features and can handle very complex data. Therefore, we propose a deep random forest-based network intrusion detection model. The first stage uses a slide window to segment original features into many small pieces and then trains a random forest to generate the concatenated class vector as rerepresentation. The vector will be used to train the multilevel cascade parallel random forest in the second stage. Finally, the classification of the original data is determined by voting strategy after the last layer of cascade. Meanwhile, the model is deployed in Spark environment and optimizes cache replacement strategy of RDDs by efficiency sorting and partition integrity check. The experiment results indicate that the proposed method can effectively detect anomaly network behaviors, with high F1-measure scores and high accuracy. The results also show that it can cut down the average execution time on different scaled clusters.