A new Boosting algorithm used in intrusion detection

DOI

10.2991/icmemtc-16.2016.243How to use a DOI?

Keywords

intrusion detection; MDBoost; overfitting; Accuracy

Abstract

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.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Zhixin Cai
AU  - Xiufen Fu
PY  - 2016/04
DA  - 2016/04
TI  - A new Boosting algorithm used in intrusion detection
BT  - Proceedings of the 2016 3rd International Conference on Materials Engineering, Manufacturing Technology and Control
PB  - Atlantis Press
SP  - 1231
EP  - 1235
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmemtc-16.2016.243
DO  - 10.2991/icmemtc-16.2016.243
ID  - Cai2016/04
ER  -