Abstract: As a key research direction in the field of machine learning, ensemble learning is widely used in anomaly intrusion detection, and it can reach a higher detection precision than the single classifier. However, existing ensemble-based intrusion detection algorithms have some shortcomings, such as, the loss of edge information as well as the loss of whole information during the process of dividing original problem, time-consuming and complexity of the model fusion. So, this paper proposed a novel ensemble-based algorithm for intrusion detection. Firstly, the original problem is divided into a number of two classification problems, and the predicted probabilities are added into original features. Then the multi-class model is trained as the final result. In addition, we adopted GBDT (Gradient Boosting Decision Tree)+LR (Logistic Regression), proposed by Facebook, to implement the binary classification. Experiments and analysis on KDD CUP’99 dataset verify the effectiveness of our proposed framework.

Key words: ensemble learning, intrusion detection, loss of information, gradient boosting decision tree (GBDT), logistic regression (LR)