An Ensemble-Based Intrusion Detection Algorithm

作者：Huang, Jinchao; Ma, Yinghua; Qi, Kaiyue; Li, Yichen; Xia, Yuanyi

来源：Journal of Shanghai Jiaotong University, 2018, 52(10): 1382-1387.

DOI：10.16183/j.cnki.jsjtu.2018.10.029

• Anomaly intrusion detection
• Binary classification
• Detection precision
• Ensemble learning
• Gradient boosting
• Intrusion detection algorithms
• Logistic regressions
• Multi-class models

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.

• 出版日期2018
• 单位上海交通大学