A new Boosting algorithm used in intrusion detection
Zhixin Cai, Xiufen Fu · 2016
At this stage, the high dimension and large variety of network data have increased the difficulty of intrusion detection.In this paper, we discuss the advantages and disadvantages of the MDBoost algorithm.Subsequently to optimize it, we add a slack variable in the objective function, so that the algorithm can effectively prevent over fitting, and the accuracy of the prediction is also improved.Then, we propose a model, which uses the MDBoost-2 algorithm to generate a strong classifier, and we use this model for intrusion detection.Finally, we use the CUP KDD 1999 data set to carry out the experiment.The results show that the new approach outperforms MDBoost and other well-known methods.