Research Article
A Novel Intrusion Detection System Based on Advanced Naive Bayesian Classification
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@INPROCEEDINGS{10.1007/978-3-319-72823-0_53, author={Yunpeng Wang and Yuzhou Li and Daxin Tian and Congyu Wang and Wenyang Wang and Rong Hui and Peng Guo and Haijun Zhang}, title={A Novel Intrusion Detection System Based on Advanced Naive Bayesian Classification}, proceedings={5G for Future Wireless Networks. First International Conference, 5GWN 2017, Beijing, China, April 21-23, 2017, Proceedings}, proceedings_a={5GWN}, year={2018}, month={1}, keywords={IDS Information security NBC ReliefF Detection performance KDD’99}, doi={10.1007/978-3-319-72823-0_53} }- Yunpeng Wang
Yuzhou Li
Daxin Tian
Congyu Wang
Wenyang Wang
Rong Hui
Peng Guo
Haijun Zhang
Year: 2018
A Novel Intrusion Detection System Based on Advanced Naive Bayesian Classification
5GWN
Springer
DOI: 10.1007/978-3-319-72823-0_53
Abstract
Intrusion Detection System is a pattern recognition task whose aim is to detect and report the occurrence of abnormal or unknown network behaviors in a given network system being monitored. In this paper, we propose a machine learning model, advanced Naive Bayesian Classification (NBC-A) which is based on NBC and ReliefF algorithm, to be used in the novel IDS. We use ReliefF algorithm to give every attribute of network behavior in KDD’99 dataset a weight that reflects the relationship between attributes and final class for better classification results. The novel IDS has a higher True Positive (TP) rate and a lower False Positive (FP) rate in detection performance.
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